Electrical Engineering and Computer Sciences < University of California, Berkeley

This is an archived copy of the 2019-20 guide. To access the most recent version of the guide, please visit http://guide.berkeley.edu.

Spring 2020 COVID-19 Measures

Overview

The Department of Electrical Engineering and Computer Sciences (EECS) offers one of the strongest research and instructional programs in this field anywhere in the world. Our key strength is our array of cross-disciplinary, team-driven projects. The integration of Electrical Engineering (EE) and Computer Science (CS) forms the core, with strong interactions that extend into biological sciences, mechanical and civil engineering, physical sciences, chemistry, mathematics, and operations research. Our programs have been consistently ranked in the top three nationwide and worldwide by various organizations.

Each year, top students from all parts of the world are attracted to the EECS program and Berkeley by the excellence of the faculty; the breadth of educational opportunities in EECS and campuswide; the proximity to the vibrant California high-tech economy; and the Berkeley environment. The department's close ties to the industry, coupled with its commitment to engineering research and education, ensure that students get a rigorous, relevant, and broad education.

Faculty members at Berkeley are committed to research and discovery at the highest level, informed and creative teaching, and the creative desire to excel. The distinction of the EECS faculty has been recognized in a long list of prestigious honors and awards, including two National Medals of Science, three ACM Turing Awards, three IEEE Medals of Honor, 36 members of the National Academy of Engineering, nine members of the National Academy of Sciences, and 16 fellows of the American Academy of Arts and Sciences.

Unlike many institutions of similar stature, regular faculty teach the vast majority of our courses, and the most exceptional teachers are often also the most exceptional researchers. The department's list of active teaching faculty includes eight winners of the prestigious Berkeley Campus Distinguished Teaching Award.

The mission of the EECS Department has three parts:

Educating future leaders in academia, government, industry, and entrepreneurial pursuit, through a rigorous curriculum of theory and application that develops the ability to solve problems, individually and in teams
Creating knowledge of fundamental principles and innovative technologies, through research within the core areas of EECS and in collaboration with other disciplines, that is distinguished by its impact on academia, industry, and society
Serving the communities to which we belong, at local, national, and international levels, with a deep awareness of our ethical responsibilities to our profession and to society

Our strategy to accomplish this mission is simple: recruit and retain the very best faculty, students, and staff, and then empower them to direct and drive the creation and dissemination of knowledge. We know that we have succeeded in this mission when our students succeed, becoming leaders and serving society.

Electrical Engineering began on the Berkeley campus more than a century ago, with the hiring of its first electrical engineer, Clarence Cory, into the College of Mechanics. The early days focused on electric power production and distribution, and Cory’s laboratory, in fact, provided the first light and power for the entire campus.

The evolution since then has been dramatic, accelerating rapidly in the latter half of the twentieth century. The development of our world-class computer science faculty followed naturally from the synergies between electronics, systems theory, and computing. In the twenty-first century, EECS has become a broader field, defined more by its intellectual approach to engineering problems than by particular technical solutions. Broadly, EECS harnesses physical processes to perform logical functions, and hence easily extends beyond its core technology base in electronics to, for example, biological systems.

Current strengths in biosystems and computational biology, nanotechnology, artificial intelligence, concurrent and distributed systems, embedded systems, novel devices (such as organic semiconductors), robotics, advanced networking, computer security and trusted computing, energy, and sensor networks, complement beautifully our traditional strengths in physical electronics, integrated circuits, operating systems and networking, graphics and human-computer interaction, communications systems, computer architecture, control theory, signal processing, the theory of computing, programming languages, scientific computing, electronic design automation, power systems, and database management systems. Many of our current research projects are focused on enormous societal challenges and opportunities such as energy efficiency, network intelligence, transportation systems, security, and health care. More than any other engineering discipline, EECS bridges the physical world and the semantic one, creating technologies to serve humanity.

Organizationally, the Department of Electrical Engineering and Computer Sciences smoothly integrates its world-class faculty with dedicated staff and extremely active and involved student groups. Our undergraduate programs recognize the daunting intellectual breadth of the field by offering a great deal of flexibility. These programs are currently (through September 30, 2019) accredited by ABET, Inc. and by the CAC, the Computing Accreditation Commission of ABET, Inc. Berkeley EECS has decided to discontinue our ABET accreditation while maintaining accreditation through the Accrediting Commission for Schools, Western Association of Schools and Colleges (ACS WASC).

Our graduate programs emphasize research, preparing students for leadership positions in industrial labs, government, or academia. Our laboratory and computing facilities are among the best anywhere and have conceived many transformative inventions. Our research programs are well funded, and nearly all of our graduate students receive full financial support.

Undergraduate Programs

Computer Science: BA (major program offered through the College of Letters and Science), Minor
Electrical Engineering and Computer Sciences: BS (with concentrations in Electrical and Computer Engineering or Computer Science and Engineering), Minor
Electrical Engineering and Computer Sciences/Materials Science and Engineering: BS (Joint Major)
Electrical Engineering and Computer Sciences/Nuclear Engineering: BS (Joint Major)

Graduate Programs

Computer Science: MS, PhD
Electrical Engineering and Computer Sciences: MEng, MS, PhD

Visit Department Website

Courses

Select a subject to view courses

Electrical Engineering and Computer Sciences
Computer Science
Electrical Engineering

Electrical Engineering and Computer Sciences

Terms offered: Fall 2020, Summer 2020 8 Week Session, Spring 2020
This course and its follow-on course EECS16B focus on the fundamentals of designing modern information devices and systems that interface with the real world. Together, this course sequence provides a comprehensive foundation for core EECS topics in signal processing, learning, control, and circuit design while introducing key linear-algebraic concepts motivated by application contexts. Modeling is emphasized in a way that deepens mathematical maturity, and in both labs and homework, students will engage computationally, physically, and visually with the concepts being introduced in addition to traditional paper/pencil exercises. The courses are aimed at entering students as well as non-majors seeking a broad foundation for the field.
Designing Information Devices and Systems I: Read More [+]

Rules & Requirements

Prerequisites: MATH 1A and MATH 1B (1B may be taken concurrently); COMPSCI 61A (encouraged to be taken concurrently)

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture, 2 hours of discussion, and 3 hours of laboratory per week

Summer: 8 weeks - 6 hours of lecture, 4 hours of discussion, and 6 hours of laboratory per week

Additional Details

Subject/Course Level: Electrical Engin and Computer Sci/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructors: Alon, Arcak, Ayazifar, Maharbiz, Niknejad, Ranade, Sahai, Subramanian, Tomlin

Formerly known as: Electrical Engineering 16A

Designing Information Devices and Systems I: Read Less [-]

Terms offered: Fall 2020, Summer 2020 8 Week Session, Spring 2020
This course is a follow-on to EECS 16A, and focuses on the fundamentals of designing and building modern information devices and systems that interface with the real world. The course sequence provides a comprehensive introduction to core EECS topics in machine learning, circuit design, control, and signal processing while developing key linear-algebraic concepts motivated by application contexts. Modeling is emphasized in a way that deepens mathematical maturity, and in both labs and homework, students will engage computationally, physically, and visually with the concepts being introduced in addition to traditional paper exercises. The courses are aimed at entering students as well as non-majors seeking a broad introduction to the field.
Designing Information Devices and Systems II: Read More [+]

Rules & Requirements

Prerequisites: EECS 16A

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture, 2 hours of discussion, and 3 hours of laboratory per week

Additional Details

Subject/Course Level: Electrical Engin and Computer Sci/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructors: Alon, Ayazifar, Lustig, Maharbiz, Subramanian, Tomlin

Formerly known as: Electrical Engineering 16B

Designing Information Devices and Systems II: Read Less [-]

Terms offered: Not yet offered
This course allows students who have had a linear algebra and/or basic circuit theory course to complete the work in EE16A and be ready for EE16B or EE47E. The course focuses on the fundamentals of designing modern information devices and systems that interface with the real world and provides a comprehensive foundation for core EECS topics in signal processing, learning, control, and circuit design. Modeling is emphasized in a way that deepens mathematical maturity, and in both labs and homework, students will engage computationally, physically, and visually with the concepts being introduced in addition to traditional paper/pencil exercises.
Completion of work in Electrical Engineering 16A: Read More [+]

Rules & Requirements

Prerequisites: MATH 1A, MATH 1B, COMPSCI 61A (encouraged to be taken concurrently), college level courses in linear algebra and/or circuit theory, and consent of the instructor

Hours & Format

Fall and/or spring: 15 weeks - 2-8 hours of self-paced per week

Summer: 8 weeks - 4-13 hours of self-paced per week

Additional Details

Subject/Course Level: Electrical Engin and Computer Sci/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructors: Alon, Arcak, Ayazifar, Maharbiz, Niknejad, Ranade, Sahai, Subramanian, Tomlin

Completion of work in Electrical Engineering 16A: Read Less [-]

Terms offered: Not yet offered
This course allows students who have had a linear algebra and/or basic circuit theory course to complete the work in EE16B. The course focuses on the fundamentals of designing modern information devices and systems that interface with the real world and provides a comprehensive foundation for core EECS topics in signal processing (DFT), learning (SVD/PCA), feedback control, and circuit design. Modeling is emphasized in a way that deepens mathematical maturity, and in both labs and homework, students will engage computationally, physically, and visually with the concepts being introduced in addition to traditional paper/pencil exercises.
Completion of work in Electrical Engineering 16B: Read More [+]

Rules & Requirements

Prerequisites: MATH 1A, MATH 1B, and COMPSCI 61A; and EECS 16A, EECS 47D, or MATH 54; college level courses in linear algebra and/or circuit theory, and consent of the instructor

Hours & Format

Fall and/or spring: 15 weeks - 3-8 hours of self-paced per week

Summer: 8 weeks - 6-16 hours of self-paced per week

Additional Details

Subject/Course Level: Electrical Engin and Computer Sci/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructors: Alon, Arcak, Ayazifar, Maharbiz, Niknejad, Ranade, Sahai, Subramanian, Tomlin

Completion of work in Electrical Engineering 16B: Read Less [-]

Terms offered: Not yet offered
This course allows students who have had a discrete math and/or probability course to complete the work in CS70. Logic, infinity, and induction; applications include undecidability and stable marriage problem. Modular arithmetic and GCDs; applications include primality testing and cryptography. Polynomials; examples include error correcting codes and interpolation. Probability including sample spaces, independence, random variables, law of large numbers; examples include load balancing, existence arguments, Bayesian inference.
Completion of work in Computer Science 70: Read More [+]

Rules & Requirements

Prerequisites: Sophomore mathematical maturity, programming experience equivalent to that gained in COMPSCI 61A, a prior college level course on discrete math and/or probability, and consent of the instructor

Hours & Format

Fall and/or spring: 15 weeks - 3-8 hours of self-paced per week

Summer: 8 weeks - 6-16 hours of self-paced per week

Additional Details

Subject/Course Level: Electrical Engin and Computer Sci/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructors: Ranade, Rao, Sahai, Seshia, Vazirani, Walrand

Completion of work in Computer Science 70: Read Less [-]

Terms offered: Fall 2020, Fall 2019, Fall 2018
An introduction to the kinematics, dynamics, and control of robot manipulators, robotic vision, and sensing. The course covers forward and inverse kinematics of serial chain manipulators, the manipulator Jacobian, force relations, dynamics, and control. It presents elementary principles on proximity, tactile, and force sensing, vision sensors, camera calibration, stereo construction, and motion detection. The course concludes with current applications of robotics in active perception, medical robotics, and other areas.
Introduction to Robotics: Read More [+]

Rules & Requirements

Prerequisites: EL ENG 120 or consent of instructor

Credit Restrictions: Students will receive no credit for Electrical Engineering and Computer Science C106A/Bioengineering C106A after completing EE C106A/BioE C125, Electrical Engineering 206A, or Electrical Engineering and Computer Science 206A.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 3 hours of laboratory per week

Summer: 8 weeks - 6 hours of lecture, 2 hours of discussion, and 6 hours of laboratory per week

Additional Details

Subject/Course Level: Electrical Engin and Computer Sci/Undergraduate

Grading/Final exam status: Letter grade. Alternative to final exam.

Instructor: Bajcsy

Also listed as: BIO ENG C106A

Introduction to Robotics: Read Less [-]

Terms offered: Spring 2020, Spring 2019, Spring 2018
This course is a sequel to EECS C106A/Bioengineering C106A, which covers kinematics, dynamics and control of a single robot. This course will cover dynamics and control of groups of robotic manipulators coordinating with each other and interacting with the environment. Concepts will include an introduction to grasping and the constrained manipulation, contacts and force control for interaction with the environment. We will also cover active perception guided manipulation, as well as the manipulation of non-rigid objects. Throughout, we will emphasize design and human-robot interactions, and applications to applications in manufacturing, service robotics, tele-surgery, and locomotion.
Robotic Manipulation and Interaction: Read More [+]

Rules & Requirements

Prerequisites: EECS C106A / BIO ENG C106A, or consent of the instructor

Credit Restrictions: Students will receive no credit for Electrical Engineering and Computer Science C106B/Bioengineering C106B after completing Electrical Engineering C106B/Bioengineering C125B, Electrical Engineering 206B, or Electrical Engineering and Computer Science 206B.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 3 hours of laboratory per week

Additional Details

Subject/Course Level: Electrical Engin and Computer Sci/Undergraduate

Grading/Final exam status: Letter grade. Alternative to final exam.

Instructors: Bajcsy, Sastry

Also listed as: BIO ENG C106B

Robotic Manipulation and Interaction: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
This course covers the fundamentals of probability and random processes useful in fields such as networks, communication, signal processing, and control. Sample space, events, probability law. Conditional probability. Independence. Random variables. Distribution, density functions. Random vectors. Law of large numbers. Central limit theorem. Estimation and detection. Markov chains.
Probability and Random Processes: Read More [+]

Rules & Requirements

Prerequisites: COMPSCI 70 preferred but not required; Familiarity with linear algebra

Credit Restrictions: Students will receive no credit for EECS 126 after completing EE 126.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Details

Subject/Course Level: Electrical Engin and Computer Sci/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructor: Ramchandran

Probability and Random Processes: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
This course offers an introduction to optimization models and their applications, ranging from machine learning and statistics to decision-making and control, with emphasis on numerically tractable problems, such as linear or constrained least-squares optimization.
Optimization Models in Engineering: Read More [+]

Rules & Requirements

Prerequisites: EECS 16A and EECS 16B, or consent of instructor

Credit Restrictions: Students will receive no credit for EECS 127 after taking EECS 227AT or Electrical Engineering 127/227AT.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Details

Subject/Course Level: Electrical Engin and Computer Sci/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructor: El Ghaoui

Formerly known as: Electrical Engineering 127

Optimization Models in Engineering: Read Less [-]

Terms offered: Fall 2020, Fall 2019, Fall 2018
This course introduces students to the basics of modeling, analysis, and design of embedded, cyber-physical systems. Students learn how to integrate computation with physical processes to meet a desired specification. Topics include models of computation, control, analysis and verification, interfacing with the physical world, real-time behaviors, mapping to platforms, and distributed embedded systems. The course has a strong laboratory component, with emphasis on a semester-long sequence of projects.
Introduction to Embedded Systems: Read More [+]

Objectives & Outcomes

Course Objectives: To develop the skills to realize embedded systems that are safe, reliable, and efficient in their use of resources.
To learn how to model and design the joint dynamics of software, networks, and physical processes.
To learn to think critically about technologies that are available for achieving such joint dynamics.

Rules & Requirements

Prerequisites: COMPSCI 61C and COMPSCI 70; EECS 16A and EECS 16B, or permission of instructor

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 3 hours of laboratory per week

Additional Details

Subject/Course Level: Electrical Engin and Computer Sci/Undergraduate

Grading/Final exam status: Letter grade. Alternative to final exam.

Instructors: Seshia, Lee

Introduction to Embedded Systems: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
An introduction to digital and system design. The material provides a top-down view of the principles, components, and methodologies for large scale digital system design. The underlying CMOS devices and manufacturing technologies are introduced, but quickly abstracted to higher-levels to focus the class on design of larger digital modules for both FPGAs (field programmable gate arrays) and ASICs (application specific integrated circuits). The class includes extensive use of industrial grade design automation and verification tools for assignments, labs and projects.
The class has two lab options: ASIC Lab (EECS 151LA) and FPGA Lab (EECS 151LB). Students must enroll in at least one of the labs concurrently with the class.
Introduction to Digital Design and Integrated Circuits: Read More [+]

Objectives & Outcomes

Course Objectives: The Verilog hardware description language is introduced and used. Basic digital system design concepts, Boolean operations/combinational logic, sequential elements and finite-state-machines, are described. Design of larger building blocks such as arithmetic units, interconnection networks, input/output units, as well as memory design (SRAM, Caches, FIFOs) and integration are also covered. Parallelism, pipelining and other micro-architectural optimizations are introduced. A number of physical design issues visible at the architecture level are covered as well, such as interconnects, power, and reliability.

Rules & Requirements

Prerequisites: EECS 16A and EECS 16B

Credit Restrictions: Students must enroll concurrently in at least one the lab flavors EECS151LA or EECS151LB. Students wishing to take a second lab flavor next term can sign-up only for that Lab section and receive a Letter grade. The pre-requisite for “Lab-only” enrollment that term will be EECS151 from previous terms.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Details

Subject/Course Level: Electrical Engin and Computer Sci/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructors: Stojanovic, Wawrzynek

Introduction to Digital Design and Integrated Circuits: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
This lab lays the foundation of modern digital design by first presenting the scripting and hardware description language base for specification of digital systems and interactions with tool flows. The labs are centered on a large design with the focus on rapid design space exploration. The lab exercises culminate with a project design, e.g., implementation of a three-stage RISC-V processor with a register file and caches. The design is mapped to simulation and layout specification.
Application Specific Integrated Circuits Laboratory: Read More [+]

Objectives & Outcomes

Course Objectives: Software testing of digital designs is covered leading to a set of exercises that cover the design flow. Digital synthesis, floor-planning, placement and routing are covered, as well as tools to evaluate timing and power consumption. Chip-level assembly is covered, including instantiation of custom blocks: I/O pads, memories, PLLs, etc.

Rules & Requirements

Prerequisites: COMPSCI 61C, EECS 16A, EECS 16B, and EL ENG 105

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of laboratory per week

Additional Details

Subject/Course Level: Electrical Engin and Computer Sci/Undergraduate

Grading/Final exam status: Letter grade. Final exam not required.

Instructors: Stojanovic, Wawrzynek

Application Specific Integrated Circuits Laboratory: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
This lab covers the design of modern digital systems with Field-Programmable Gate Array (FPGA) platforms. A series of lab exercises provide the background and practice of digital design using a modern FPGA design tool flow. Digital synthesis, partitioning, placement, routing, and simulation tools for FPGAs are covered in detail. The labs exercises culminate with a large design project, e.g., an implementation of a full three-stage RISC-V processor system, with caches, graphics acceleration, and external peripheral components. The design is mapped and demonstrated on an FPGA hardware platform.
Field-Programmable Gate Array Laboratory: Read More [+]

Rules & Requirements

Prerequisites: EECS 16A, EECS 16B, and COMPSCI 61C; EL ENG 105 recommended

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of laboratory per week

Additional Details

Subject/Course Level: Electrical Engin and Computer Sci/Undergraduate

Grading/Final exam status: Letter grade. Final exam not required.

Instructors: Stojanovic, Wawrzynek

Field-Programmable Gate Array Laboratory: Read Less [-]

Terms offered: Fall 2020, Fall 2019, Fall 2018
An introduction to the kinematics, dynamics, and control of robot manipulators, robotic vision, and sensing. The course will cover forward and inverse kinematics of serial chain manipulators, the manipulator Jacobian, force relations, dynamics and control-position, and force control. Proximity, tactile, and force sensing. Network modeling, stability, and fidelity in teleoperation and medical applications of robotics.
Introduction to Robotics: Read More [+]

Rules & Requirements

Prerequisites: EE 120 or equivalent, or consent of instructor

Credit Restrictions: Students will receive no credit for EECS 206A after taking EE C125/Bioengineering C125, EE C106A, or EECS C106A.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 3 hours of laboratory per week

Additional Details

Subject/Course Level: Electrical Engin and Computer Sci/Graduate

Grading: Letter grade.

Instructor: Bajcsy

Introduction to Robotics: Read Less [-]

Terms offered: Spring 2020, Spring 2019, Spring 2018
This course is a sequel to EECS C106A/206A, which covers kinematics,
dynamics and control of a single robot. This course will cover dynamics and control of groups of robotic
manipulators coordinating with each other and interacting with the environment. Concepts will include
an introduction to grasping and the constrained manipulation, contacts and force control for interaction
with the environment. We will also cover active perception guided manipulation, as well as the
manipulation of non-rigid objects. Throughout, we will emphasize design and human-robot
interactions, and applications to applications in manufacturing, service robotics, tele-surgery, and
locomotion.
Robotic Manipulation and Interaction: Read More [+]

Rules & Requirements

Prerequisites: EECS C106A/Bioengineering C106A, EECS 206A or consent of the instructor

Credit Restrictions: Students will receive no credit for EECS 206B after taking EE C106B/Bioengineering C125B, EECS C106B/BioEngineering C106B, or EE 206B.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 3 hours of laboratory per week

Additional Details

Subject/Course Level: Electrical Engin and Computer Sci/Graduate

Grading: Letter grade.

Instructors: Bajcsy, Sastry

Robotic Manipulation and Interaction: Read Less [-]

Terms offered: Spring 2020, Spring 2019, Spring 2018
Introduction to the theory and practice of formal methods for the design and analysis of systems, with a focus on algorithmic techniques. Covers selected topics in computational logic and automata theory including modeling and specification formalisms, temporal logics, satisfiability solving, model checking, synthesis, learning, and theorem proving. Applications to software and hardware design, cyber-physical systems, robotics, computer security, and other areas will be explored as time permits.
Formal Methods: Specification, Verification, and Synthesis: Read More [+]

Rules & Requirements

Prerequisites: Graduate standing or Consent of instructor; Computer Science 170 or equivalent is recommended

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture per week

Additional Details

Subject/Course Level: Electrical Engin and Computer Sci/Graduate

Grading: Letter grade.

Instructor: Seshia

Formerly known as: Electrical Engineering 219C

Formal Methods: Specification, Verification, and Synthesis: Read Less [-]

Terms offered: Fall 2020, Spring 2020
This course connects classical statistical signal processing (Hilbert space filtering theory by Wiener and Kolmogorov, state space model, signal representation, detection and estimation, adaptive filtering) with modern statistical and machine learning theory and applications. It focuses on concrete algorithms and combines principled theoretical thinking with real applications.
Statistical Signal Processing: Read More [+]

Rules & Requirements

Prerequisites: ELENG 120 and EECS 126

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture per week

Additional Details

Subject/Course Level: Electrical Engin and Computer Sci/Graduate

Grading: Letter grade.

Instructors: Jiao, Waller

Formerly known as: Electrical Engineering 225A

Statistical Signal Processing: Read Less [-]

Terms offered: Fall 2020, Spring 2020
This course deals with computational methods as applied to digital imagery. It focuses on image sensing and acquisition, image sampling and quantization; spatial transformation, linear and nonlinear filtering; introduction to convolutional neural networks, and GANs; applications of deep learning methods to image processing problems; image enhancement, histogram equalization, image restoration, Weiner filtering, tomography, image reconstruction from projections and partial Fourier information, Radon transform, multiresolution analysis, continuous and discrete wavelet transform and computation, subband coding, image and video compression, sparse signal approximation, dictionary techniques, image and video compression standards, and more.
Digital Image Processing: Read More [+]

Rules & Requirements

Prerequisites: Basic knowledge of signals and systems, convolution, and Fourier Transform

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture per week

Additional Details

Subject/Course Level: Electrical Engin and Computer Sci/Graduate

Grading: Letter grade.

Instructor: Zakhor

Formerly known as: Electrical Engineering 225B

Digital Image Processing: Read Less [-]

Rules & Requirements

Prerequisites: Mathematics 54 or equivalent or consent of instructor

Credit Restrictions: Students will receive no credit for EECS 227AT after taking EECS 127 or Electrical Engineering 127/227AT.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Details

Subject/Course Level: Electrical Engin and Computer Sci/Graduate

Grading: Letter grade.

Instructor: El Ghaoui

Formerly known as: Electrical Engineering 227AT

Optimization Models in Engineering: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
An introduction to digital circuit and system design. The material provides a top-down view of the principles, components, and methodologies for large scale digital system design. The underlying CMOS devices and manufacturing technologies are introduced, but quickly abstracted to higher levels to focus the class on design of larger digital modules for both FPGAs (field programmable gate arrays) and ASICs (application specific integrated circuits). The class includes extensive use of industrial grade design automation and verification tools for assignments, labs, and projects.
Introduction to Digital Design and Integrated Circuits: Read More [+]

Objectives & Outcomes

Student Learning Outcomes: Although the syllabus is the same as EECS151, the assignments and exams for EECS251A will have harder problems that test deeper understanding expected from a graduate level course.

Rules & Requirements

Prerequisites: Electrical Engineering 16A & 16B; Computer Science 61C; and recommended: Electrical Engineering 105. Students must enroll concurrently in at least one the laboratory flavors Electrical Engineering and Computer Science 251LA or Electrical Engineering and Computer Science 251LB. Students wishing to take a second laboratory flavor next term can sign-up only for that laboratory section and receive a letter grade. The pre-requisite for “Lab-only” enrollment that term will be Electrical Engineering an

Credit Restrictions: Students must enroll concurrently in at least one the laboratory flavors Electrical Engineering and Computer Science 251LA or Electrical Engineering and Computer Science 251LB. Students wishing to take a second laboratory flavor next term can sign-up only for that laboratory section and receive a letter grade. The pre-requisite for “Lab-only” enrollment that term will be Electrical Engineering and Computer Science 251A from previous terms.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Details

Subject/Course Level: Electrical Engin and Computer Sci/Graduate

Grading: Letter grade.

Instructors: Stojanovic, Wawrzynek

Formerly known as: Electrical Engineering 241A

Introduction to Digital Design and Integrated Circuits: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
This lab lays the foundation of modern digital design by first presenting the scripting and hardware description language base for specification of digital systems and interactions with tool flows. The labs are centered on a large design with the focus on rapid design space exploration. The lab exercises culminate with a project design, e.g. implementation of a 3-stage RISC-V processor with a register file and caches. The design is mapped to simulation and layout specification.
Introduction to Digital Design and Integrated Circuits Lab: Read More [+]

Objectives & Outcomes

Student Learning Outcomes: Although the syllabus is the same as EECS151LA, the assignments and exams for EECS251LA will have harder problems in labs and in the project that test deeper understanding expected from a graduate level course.

Rules & Requirements

Prerequisites: Electrical Engineering 16A & 16B; Computer Science 61C; and recommended: Electrical Engineering 105

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of laboratory per week

Additional Details

Subject/Course Level: Electrical Engin and Computer Sci/Graduate

Grading: Letter grade.

Instructors: Stojanovic, Wawrzynek

Introduction to Digital Design and Integrated Circuits Lab: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
This lab covers the design of modern digital systems with Field-Programmable Gate Array (FPGA) platforms. A series of lab exercises provide the background and practice of digital design using a modern FPGA design tool flow. Digital synthesis, partitioning, placement, routing, and simulation tools for FPGAs are covered in detail. The labs exercises culminate with a large design project, e.g., an implementation of a full 3-stage RISC-V processor system, with caches, graphics acceleration, and external peripheral components. The design is mapped and demonstrated on an FPGA hardware platform.
Introduction to Digital Design and Integrated Circuits Lab: Read More [+]

Objectives & Outcomes

Student Learning Outcomes: Although the syllabus is the same as EECS151LB, the assignments and exams for EECS251LB will have harder problems in labs and in the project that test deeper understanding expected from a graduate level course.

Rules & Requirements

Prerequisites: Electrical Engineering 16A & 16B; Computer Science 61C; and recommended: Electrical Engineering 105

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of laboratory per week

Additional Details

Subject/Course Level: Electrical Engin and Computer Sci/Graduate

Grading: Letter grade.

Instructors: Stojanovic, Wawrzynek

Introduction to Digital Design and Integrated Circuits Lab: Read Less [-]

Computer Science

Terms offered: Not yet offered
An introduction to computational thinking and quantitative reasoning, preparing students for further coursework, especially Foundations of Data Science (CS/Info/Stat C8). Emphasizes the use of computation to gain insight about quantitative problems with real data. Expressions, data types, collections, and tables in Python. Programming practices, abstraction, and iteration. Visualizing univariate and bivariate data with bar charts, histograms, plots, and maps. Introduction to statistical concepts including averages and distributions, predicting one variable from another, association and causality, probability and probabilistic simulation. Relationship between numerical functions and graphs. Sampling and introduction to inference.
Introduction to Computational Thinking with Data: Read More [+]

Objectives & Outcomes

Course Objectives: C6 also includes quantitative reasoning concepts that aren’t covered in Data 8. These include certain topics in: principles of data visualization; simulation of random processes; and understanding numerical functions through their graphs. This will help prepare students for computational and quantitative courses other than Data 8.
C6 takes advantage of the complementarity of computing and quantitative reasoning to enliven abstract ideas and build students’ confidence in their ability to solve real problems with quantitative tools. Students learn computer science concepts and immediately apply them to plot functions, visualize data, and simulate random events.

Foundations of Data Science (CS/Info/Stat C8, a.k.a. Data 8) is an increasingly popular class for entering students at Berkeley. Data 8 builds students’ computing skills in the first month of the semester, and students rely on these skills as the course progresses. For some students, particularly those with little prior exposure to computing, developing these skills benefits from further time and practice. C6 is a rapid introduction to Python programming, visualization, and data analysis, which will prepare students for success in Data 8.

Student Learning Outcomes: Students will be able to perform basic computations in Python, including working with tabular data.
Students will be able to understand basic probabilistic simulations.
Students will be able to understand the syntactic structure of Python code.
Students will be able to use good practices in Python programming.
Students will be able to use visualizations to understand univariate data and to identify associations or causal relationships in bivariate data.

Rules & Requirements

Credit Restrictions: Students will receive no credit for DATA C6\COMPSCI C6\STAT C6 after completing DATA C8, or DATA 6. A deficient grade in DATA C6\COMPSCI C6\STAT C6 may be removed by taking DATA 6.

Hours & Format

Summer: 6 weeks - 4 hours of lecture, 2 hours of discussion, and 4 hours of laboratory per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Formerly known as: Computer Science C8R/Statistics C8R

Also listed as: DATA C6/STAT C6

Introduction to Computational Thinking with Data: Read Less [-]

Terms offered: Fall 2020, Summer 2020 8 Week Session, Spring 2020, Fall 2019, Spring 2019
Foundations of data science from three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social and legal issues surrounding data analysis, including issues of privacy and data ownership.
Foundations of Data Science: Read More [+]

Rules & Requirements

Prerequisites: This course may be taken on its own, but students are encouraged to take it concurrently with a data science connector course (numbered 88 in a range of departments)

Credit Restrictions: Students will receive no credit for DATA C8\COMPSCI C8\INFO C8\STAT C8 after completing COMPSCI 8, or DATA 8. A deficient grade in DATA C8\COMPSCI C8\INFO C8\STAT C8 may be removed by taking COMPSCI 8, COMPSCI 8, or DATA 8.

Hours & Format

Fall and/or spring: 15 weeks - 3-3 hours of lecture and 2-2 hours of laboratory per week

Summer: 8 weeks - 6 hours of lecture and 4 hours of laboratory per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Formerly known as: Computer Science C8/Statistics C8/Information C8

Also listed as: DATA C8/INFO C8/STAT C8

Foundations of Data Science: Read Less [-]

Terms offered: Fall 2018, Spring 2018, Fall 2017
Introduction to the constructs in the Matlab programming language, aimed at students who already know how to program. Array and matrix operations, functions and function handles, control flow, plotting and image manipulation, cell arrays and structures, and the Symbolic Mathematics toolbox.
Matlab for Programmers: Read More [+]

Rules & Requirements

Prerequisites: Programming experience equivalent to that gained in COMPSCI 10; familiarity with applications of matrix processing

Repeat rules: Course may be repeated for credit up to a total of 4 units.

Hours & Format

Fall and/or spring: 15 weeks - 2 hours of self-paced per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Offered for pass/not pass grade only. Final exam required.

Instructor: Hilfinger

Matlab for Programmers: Read Less [-]

Terms offered: Spring 2019, Fall 2018, Spring 2018
Self-paced course in the C programming language for students who already know how to program. Computation, input and output, flow of control, functions, arrays, and pointers, linked structures, use of dynamic storage, and implementation of abstract data types.
C for Programmers: Read More [+]

Rules & Requirements

Prerequisites: Programming experience with pointers (or addresses in assembly language) and linked data structures equivalent to that gained in COMPSCI 9B, COMPSCI 61A or ENGIN 7

Credit Restrictions: Students will receive no credit for COMPSCI 9C after completing COMPSCI 61A.

Hours & Format

Fall and/or spring: 15 weeks - 2 hours of self-paced per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Offered for pass/not pass grade only. Final exam required.

Instructor: Hilfinger

C for Programmers: Read Less [-]

Terms offered: Spring 2016, Fall 2015, Spring 2015
Self-paced course in functional programming, using the Scheme programming language, for students who already know how to program. Recursion; higher-order functions; list processing; implementation of rule-based querying.
Scheme and Functional Programming for Programmers: Read More [+]

Rules & Requirements

Prerequisites: Programming experience similar to that gained in COMPSCI 10 or ENGIN 7

Credit Restrictions: Students will receive no credit for COMPSCI 9D after completing COMPSCI 61A.

Hours & Format

Fall and/or spring: 15 weeks - 2 hours of self-paced per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Offered for pass/not pass grade only. Final exam required.

Instructor: Hilfinger

Scheme and Functional Programming for Programmers: Read Less [-]

Terms offered: Spring 2019, Fall 2018, Spring 2018
Use of UNIX utilities and scripting facilities for customizing the programming environment, organizing files (possibly in more than one computer account), implementing a personal database, reformatting text, and searching for online resources.
Productive Use of the UNIX Environment: Read More [+]

Rules & Requirements

Prerequisites: Programming experience similar to that gained in COMPSCI 61A or ENGIN 7; DOS or UNIX experience

Hours & Format

Fall and/or spring: 15 weeks - 2 hours of self-paced per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Offered for pass/not pass grade only. Final exam required.

Instructor: Hilfinger

Productive Use of the UNIX Environment: Read Less [-]

Terms offered: Spring 2019, Fall 2018, Spring 2018
Self-paced introduction to the constructs provided in the C++ programming language for procedural and object-oriented programming, aimed at students who already know how to program.
C++ for Programmers: Read More [+]

Rules & Requirements

Prerequisites: Programming experience equivalent to that gained in COMPSCI 61A or ENGIN 7

Credit Restrictions: Students will receive no credit for COMPSCI 9F after completing COMPSCI 61A.

Hours & Format

Fall and/or spring: 15 weeks - 2 hours of self-paced per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Offered for pass/not pass grade only. Final exam required.

Instructor: Hilfinger

C++ for Programmers: Read Less [-]

Terms offered: Spring 2019, Fall 2018, Spring 2018
Self-paced course in Java for students who already know how to program. Applets; variables and computation; events and flow of control; classes and objects; inheritance; GUI elements; applications; arrays, strings, files, and linked structures; exceptions; threads.
JAVA for Programmers: Read More [+]

Rules & Requirements

Prerequisites: COMPSCI 9C, COMPSCI 9F, or COMPSCI 61A plus experience with object-oriented programming or C-based language

Hours & Format

Fall and/or spring: 15 weeks - 2 hours of self-paced per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Offered for pass/not pass grade only. Final exam required.

Instructor: Garcia

JAVA for Programmers: Read Less [-]

Terms offered: Spring 2019, Fall 2018, Spring 2018
Introduction to the constructs provided in the Python programming language, aimed at students who already know how to program. Flow of control; strings, tuples, lists, and dictionaries; CGI programming; file input and output; object-oriented programming; GUI elements.
Python for Programmers: Read More [+]

Rules & Requirements

Prerequisites: Programming experience equivalent to that gained in COMPSCI 10

Hours & Format

Fall and/or spring: 15 weeks - 1 hour of self-paced per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Offered for pass/not pass grade only. Final exam required.

Instructor: Hilfinger

Python for Programmers: Read Less [-]

Terms offered: Fall 2020, Summer 2020 8 Week Session, Spring 2020
An introductory course for students with minimal prior exposure to computer science. Prepares students for future computer science courses and empowers them to utilize programming to solve problems in their field of study. Presents an overview of the history, great principles, and transformative applications of computer science, as well as a comprehensive introduction to programming. Topics include abstraction, recursion, algorithmic complexity, higher-order functions, concurrency, social implications of computing (privacy, education, algorithmic bias), and engaging research areas (data science, AI, HCI). Students will program in Snap! (a friendly graphical language) and Python, and will design and implement two projects of their choice.
The Beauty and Joy of Computing: Read More [+]

Rules & Requirements

Credit Restrictions: Students will receive no credit for 10 after having taken W10, 61A, 61B, or 61C.

Hours & Format

Fall and/or spring: 15 weeks - 2 hours of lecture, 1 hour of discussion, and 4 hours of laboratory per week

Summer: 8 weeks - 4 hours of lecture, 2 hours of discussion, and 8 hours of laboratory per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructors: Garcia, Hug

The Beauty and Joy of Computing: Read Less [-]

Terms offered: Fall 2012
This course meets the programming prerequisite for 61A. An introduction to the beauty and joy of computing. The history, social implications, great principles, and future of computing. Beautiful applications that have changed the world. How computing empowers discovery and progress in other fields. Relevance of computing to the student and society will be emphasized. Students will learn the joy of programming a computer using a friendly, graphical language, and will complete a substantial team programming project related to their interests.
The Beauty and Joy of Computing: Read More [+]

Rules & Requirements

Credit Restrictions: Students will receive no credit for W10 after taking 10, 61A, 61B or 61C. A deficient grade in 10 may be removed by taking W10.

Hours & Format

Fall and/or spring: 15 weeks - 2 hours of web-based lecture and 5 hours of web-based discussion per week

Summer: 8 weeks - 4 hours of web-based lecture and 10 hours of web-based discussion per week

Online: This is an online course.

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructors: Garcia, Hug

The Beauty and Joy of Computing: Read Less [-]

Terms offered: Fall 2019, Fall 2018, Spring 2018
Computer Science 36 is a seminar for CS Scholars who are concurrently taking CS61A: The Structure and Interpretation of Computer Programs. CS Scholars is a cohort-model program to provide support in exploring and potentially declaring a CS major for students with little to no computational background prior to coming to the university. CS 36 provides an introduction to the CS curriculum at UC Berkeley, and the overall CS landscape in both industry and academia—through the lens of accessibility and its relevance to diversity. Additionally, CS36 provides technical instruction to review concepts in CS61A, in order to support CS Scholars’ individual learning and success in the CS61A course.
CS Scholars Seminar: The Educational Climate in CS & CS61A technical discussions: Read More [+]

Objectives & Outcomes

Student Learning Outcomes: Students will know where to find several support services including tutoring, advising, counseling, and career advice.
Students will perform as well as possible in the CS61A prerequisite for the CS major. They will also have customized program plans for completing the major within four years.

Rules & Requirements

Prerequisites: Prerequisite satisfied Concurrently: Participating in the CS Scholars program, and concurrently taking COMPSCI 61A

Hours & Format

Fall and/or spring: 15 weeks - 2 hours of seminar per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Offered for pass/not pass grade only. Alternative to final exam.

Instructor: Hunn

CS Scholars Seminar: The Educational Climate in CS & CS61A technical discussions: Read Less [-]

Terms offered: Spring 2019, Fall 2017, Spring 2017
Freshman and sophomore seminars offer lower division students the opportunity to explore an intellectual topic with a faculty member and a group of peers in a small-seminar setting. These seminars are offered in all campus departments; topics vary from department to department and from semester to semester. Enrollment limits are set by the faculty, but the suggested limit is 25.
Freshman/Sophomore Seminar: Read More [+]

Rules & Requirements

Prerequisites: Priority given to freshmen and sophomores

Repeat rules: Course may be repeated for credit when topic changes.

Hours & Format

Fall and/or spring: 15 weeks - 2-3 hours of seminar per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Offered for pass/not pass grade only. Final Exam To be decided by the instructor when the class is offered.

Freshman/Sophomore Seminar: Read Less [-]

Terms offered: Fall 2010, Spring 2010, Fall 2009
Freshman and sophomore seminars offer lower division students the opportunity to explore an intellectual topic with a faculty member and a group of peers in a small-seminar setting. These seminars are offered in all campus departments; topics vary from department to department and from semester to semester. Enrollment limits are set by the faculty, but the suggested limit is 25.
Freshman/Sophomore Seminar: Read More [+]

Rules & Requirements

Prerequisites: Priority given to freshmen and sophomores

Repeat rules: Course may be repeated for credit without restriction.

Hours & Format

Fall and/or spring: 15 weeks - 2-4 hours of seminar per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: The grading option will be decided by the instructor when the class is offered. Final exam required.

Freshman/Sophomore Seminar: Read Less [-]

Terms offered: Spring 2013, Spring 2011, Spring 2010
Freshman and sophomore seminars offer lower division students the opportunity to explore an intellectual topic with a faculty member and a group of peers in a small-seminar setting. These seminars are offered in all campus departments; topics vary from department to department and from semester to semester. Enrollment limits are set by the faculty, but the suggested limit is 25.
Freshman/Sophomore Seminar: Read More [+]

Rules & Requirements

Prerequisites: Priority given to freshmen and sophomores

Repeat rules: Course may be repeated for credit without restriction.

Hours & Format

Fall and/or spring: 15 weeks - 2-4 hours of seminar per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: The grading option will be decided by the instructor when the class is offered. Final exam required.

Freshman/Sophomore Seminar: Read Less [-]

Terms offered: Fall 2008
Freshman and sophomore seminars offer lower division students the opportunity to explore an intellectual topic with a faculty member and a group of peers in a small-seminar setting. These seminars are offered in all campus departments; topics vary from department to department and from semester to semester. Enrollment limits are set by the faculty, but the suggested limit is 25.
Freshman/Sophomore Seminar: Read More [+]

Rules & Requirements

Prerequisites: Priority given to freshmen and sophomores

Repeat rules: Course may be repeated for credit without restriction.

Hours & Format

Fall and/or spring: 15 weeks - 2-4 hours of seminar per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: The grading option will be decided by the instructor when the class is offered. Final exam required.

Freshman/Sophomore Seminar: Read Less [-]

Terms offered: Fall 2010, Fall 2009
Freshman and sophomore seminars offer lower division students the opportunity to explore an intellectual topic with a faculty member and a group of peers in a small-seminar setting. These seminars are offered in all campus departments; topics vary from department to department and from semester to semester. Enrollment limits are set by the faculty, but the suggested limit is 25.
Freshman/Sophomore Seminar: Read More [+]

Rules & Requirements

Prerequisites: Priority given to freshmen and sophomores

Repeat rules: Course may be repeated for credit without restriction.

Hours & Format

Fall and/or spring: 15 weeks - 2-4 hours of seminar per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: The grading option will be decided by the instructor when the class is offered. Final exam required.

Freshman/Sophomore Seminar: Read Less [-]

Terms offered: Fall 2013, Spring 2013, Fall 2012
Freshman and sophomore seminars offer lower division students the opportunity to explore an intellectual topic with a faculty member and a group of peers in a small-seminar setting. These seminars are offered in all campus departments; topics vary from department to department and from semester to semester. Enrollment limits are set by the faculty, but the suggested limit is 25.
Freshman/Sophomore Seminar: Read More [+]

Rules & Requirements

Prerequisites: Priority given to freshmen and sophomores

Repeat rules: Course may be repeated for credit without restriction.

Hours & Format

Fall and/or spring: 15 weeks - 2-4 hours of seminar per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: The grading option will be decided by the instructor when the class is offered. Final exam required.

Freshman/Sophomore Seminar: Read Less [-]

Terms offered: Fall 2011
Freshman and sophomore seminars offer lower division students the opportunity to explore an intellectual topic with a faculty member and a group of peers in a small-seminar setting. These seminars are offered in all campus departments; topics vary from department to department and from semester to semester. Enrollment limits are set by the faculty, but the suggested limit is 25.
Freshman/Sophomore Seminar: Read More [+]

Rules & Requirements

Prerequisites: Priority given to freshmen and sophomores

Repeat rules: Course may be repeated for credit without restriction.

Hours & Format

Fall and/or spring: 15 weeks - 2-4 hours of seminar per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: The grading option will be decided by the instructor when the class is offered. Final exam required.

Freshman/Sophomore Seminar: Read Less [-]

Terms offered: Spring 2016, Spring 2013
Freshman and sophomore seminars offer lower division students the opportunity to explore an intellectual topic with a faculty member and a group of peers in a small-seminar setting. These seminars are offered in all campus departments; topics vary from department to department and from semester to semester. Enrollment limits are set by the faculty, but the suggested limit is 25.
Freshman/Sophomore Seminar: Read More [+]

Rules & Requirements

Prerequisites: Priority given to freshmen and sophomores

Repeat rules: Course may be repeated for credit without restriction.

Hours & Format

Fall and/or spring: 15 weeks - 2-4 hours of seminar per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: The grading option will be decided by the instructor when the class is offered. Final exam required.

Freshman/Sophomore Seminar: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
Implementation of generic operations. Streams and iterators. Implementation techniques for supporting functional, object-oriented, and constraint-based programming in the Scheme programming language. Together with 9D, 47A constitutes an abbreviated, self-paced version of 61A for students who have already taken a course equivalent to 61B.
Completion of Work in Computer Science 61A: Read More [+]

Rules & Requirements

Prerequisites: COMPSCI 61B, COMPSCI 9D, and consent of instructor

Credit Restrictions: Students will receive no credit for 47A after taking 61A.

Hours & Format

Fall and/or spring: 15 weeks - 0 hours of self-paced per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructor: Garcia

Completion of Work in Computer Science 61A: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
Iterators. Hashing, applied to strings and multi-dimensional structures. Heaps. Storage management. Design and implementation of a program containing hundreds of lines of code. Students who have completed a portion of the subject matter of COMPSCI 61B may, with consent of instructor, complete COMPSCI 61B in this self-paced course. Please note that students in the College of Engineering are required to receive additional permission from the College as well as the EECS department for the course to count in place of COMPSCI 61B.
Completion of Work in Computer Science 61B: Read More [+]

Rules & Requirements

Prerequisites: A course in data structures, COMPSCI 9G, and consent of instructor

Credit Restrictions: Students will receive no credit for 47B after taking 61B.

Hours & Format

Fall and/or spring: 15 weeks - 0 hours of self-paced per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructor: Garcia

Completion of Work in Computer Science 61B: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
MIPS instruction set simulation. The assembly and linking process. Caches and virtual memory. Pipelined computer organization. Students with sufficient partial credit in 61C may, with consent of instructor, complete the credit in this self-paced course.
Completion of Work in Computer Science 61C: Read More [+]

Rules & Requirements

Prerequisites: Experience with assembly language including writing an interrupt handler, COMPSCI 9C, and consent of instructor

Credit Restrictions: Students will receive no credit for COMPSCI 47C after completing COMPSCI 61C, or COMPSCI 61CL.

Hours & Format

Fall and/or spring: 15 weeks - 0 hours of self-paced per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructor: Garcia

Completion of Work in Computer Science 61C: Read Less [-]

Terms offered: Fall 2020, Summer 2020 8 Week Session, Spring 2020
An introduction to programming and computer science focused on abstraction techniques as means to manage program complexity. Techniques include procedural abstraction; control abstraction using recursion, higher-order functions, generators, and streams; data abstraction using interfaces, objects, classes, and generic operators; and language abstraction using interpreters and macros. The course exposes students to programming paradigms, including functional, object-oriented, and declarative approaches. It includes an introduction to asymptotic analysis of algorithms. There are several significant programming projects.
The Structure and Interpretation of Computer Programs: Read More [+]

Rules & Requirements

Prerequisites: MATH 1A (may be taken concurrently); programming experience equivalent to that gained from a score of 3 or above on the Advanced Placement Computer Science A exam

Credit Restrictions: Students will receive no credit for Computer Science 61A after completing Computer Science 47A or Computer Science 61AS. A deficient grade in Computer Science 61AS may be removed by taking Computer Science 61A.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture, 1.5 hours of discussion, and 1.5 hours of laboratory per week

Summer: 8 weeks - 6 hours of lecture, 3 hours of discussion, and 3 hours of laboratory per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructors: Garcia, Hilfinger

The Structure and Interpretation of Computer Programs: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
Fundamental dynamic data structures, including linear lists, queues, trees, and other linked structures; arrays strings, and hash tables. Storage management. Elementary principles of software engineering. Abstract data types. Algorithms for sorting and searching. Introduction to the Java programming language.
Data Structures: Read More [+]

Rules & Requirements

Prerequisites: COMPSCI 61A, COMPSCI 88, or ENGIN 7

Credit Restrictions: Students will receive no credit for COMPSCI 61B after completing COMPSCI 61BL, or COMPSCI 47B. A deficient grade in COMPSCI 61B may be removed by taking COMPSCI 61BL.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 2 hours of laboratory per week

Summer: 8 weeks - 6 hours of lecture, 2 hours of discussion, and 4 hours of laboratory per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructors: Hilfinger, Shewchuk

Data Structures: Read Less [-]

Terms offered: Summer 2020 8 Week Session, Summer 2019 8 Week Session, Summer 2018 8 Week Session
The same material as in 61B, but in a laboratory-based format.
Data Structures and Programming Methodology: Read More [+]

Rules & Requirements

Prerequisites: COMPSCI 61A, COMPSCI 88, or ENGIN 7

Credit Restrictions: Students will receive no credit for 61BL after taking 47B or 61B. Deficiency in 61B may be removed by taking 61BL.

Hours & Format

Fall and/or spring: 15 weeks - 1 hour of lecture and 6 hours of laboratory per week

Summer: 8 weeks - 2 hours of lecture and 12 hours of laboratory per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructor: Hilfinger

Data Structures and Programming Methodology: Read Less [-]

Terms offered: Fall 2020, Summer 2020 8 Week Session, Spring 2020
The internal organization and operation of digital computers. Machine architecture, support for high-level languages (logic, arithmetic, instruction sequencing) and operating systems (I/O, interrupts, memory management, process switching). Elements of computer logic design. Tradeoffs involved in fundamental architectural design decisions.
Great Ideas of Computer Architecture (Machine Structures): Read More [+]

Rules & Requirements

Prerequisites: COMPSCI 61A, along with either COMPSCI 61B or COMPSCI 61BL, or programming experience equivalent to that gained in COMPSCI 9C, COMPCI 9F, or COMPSCI 9G

Credit Restrictions: Students will receive no credit for COMPSCI 61C after completing COMPSCI 61CL.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 2 hours of laboratory per week

Summer: 8 weeks - 6 hours of lecture, 2 hours of discussion, and 4 hours of laboratory per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructors: Garcia, Katz, Stojanovic

Great Ideas of Computer Architecture (Machine Structures): Read Less [-]

Terms offered: Fall 2009, Spring 2009, Fall 2008
The same material as in 61C but in a lab-centric format.
Machine Structures (Lab-Centric): Read More [+]

Rules & Requirements

Prerequisites: COMPSCI 61A, along with COMPSCI 61B or COMPSCI 61BL, or programming experience equivalent to that gained in COMPSCI 9C, COMPSCI 9F, or COMPSCI 9G

Credit Restrictions: Students will receive no credit for COMPSCI 61CL after completing COMPSCI 61C, or COMPSCI 47C.

Hours & Format

Fall and/or spring: 15 weeks - 2 hours of lecture, 1 hour of discussion, and 4 hours of laboratory per week

Summer: 8 weeks - 4 hours of lecture, 2 hours of discussion, and 8 hours of laboratory per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructors: Garcia, Patterson

Machine Structures (Lab-Centric): Read Less [-]

Terms offered: Summer 2019 8 Week Session
An introduction to programming and computer science focused on abstraction techniques as means to manage program complexity. Techniques include procedural abstraction; control abstraction using recursion, higher-order functions, generators, and streams; data abstraction using interfaces, objects, classes, and generic operators; and language abstraction using interpreters and macros. The course exposes students to programming paradigms, including functional, object-oriented, and declarative approaches. It includes an introduction to asymptotic analysis of algorithms. There are several significant programming projects.
The Structure and Interpretation of Computer Programs (Online): Read More [+]

Rules & Requirements

Prerequisites: MATH 1A (may be taken concurrently); programming experience equivalent to that gained from a score of 3 or above on the Advanced Placement Computer Science A exam

Credit Restrictions: Students will receive no credit for Computer Science W61A after completing Computer Science 47A or Computer Science 61A. A deficient grade in Computer Science W61A may be removed by taking Computer Science 61A.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of web-based lecture, 1.5 hours of laboratory, and 1.5 hours of web-based discussion per week

Summer: 8 weeks - 6 hours of web-based lecture, 3 hours of laboratory, and 3 hours of web-based discussion per week

Online: This is an online course.

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructor: Denero

The Structure and Interpretation of Computer Programs (Online): Read Less [-]

Terms offered: Not yet offered
Identical to CS61B, but in an online format. Fundamental dynamic data structures, including linear lists, queues, trees, and other linked structures; arrays strings, and hash tables. Storage management. Elementary principles of software engineering. Abstract data types. Algorithms for sorting and searching. Introduction to the Java programming language.
Data Structures (Online): Read More [+]

Rules & Requirements

Prerequisites: COMPSCI 61A, COMPSCI W61A, COMPSCI 88 or ENGIN 7

Credit Restrictions: Students will receive no credit for COMPSCI W61B after completing COMPSCI 61B. A deficient grade in COMPSCI W61B may be removed by taking COMPSCI 61B.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of web-based lecture, 2 hours of laboratory, and 1 hour of web-based discussion per week

Summer: 8 weeks - 6 hours of web-based lecture, 4 hours of laboratory, and 2 hours of web-based discussion per week

Online: This is an online course.

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructor: Hug

Data Structures (Online): Read Less [-]

Terms offered: Fall 2020, Summer 2020 8 Week Session, Spring 2020
Logic, infinity, and induction; applications include undecidability and stable marriage problem. Modular arithmetic and GCDs; applications include primality testing and cryptography. Polynomials; examples include error correcting codes and interpolation. Probability including sample spaces, independence, random variables, law of large numbers; examples include load balancing, existence arguments, Bayesian inference.
Discrete Mathematics and Probability Theory: Read More [+]

Rules & Requirements

Prerequisites: Sophomore mathematical maturity, and programming experience equivalent to that gained with a score of 3 or above on the Advanced Placement Computer Science A exam

Credit Restrictions: Students will receive no credit for Computer Science 70 after taking Mathematics 55.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of discussion per week

Summer: 8 weeks - 6 hours of lecture and 4 hours of discussion per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructors: Rao, Vazirani, Wagner, Sahai

Discrete Mathematics and Probability Theory: Read Less [-]

Terms offered: Spring 2013
Defining, perceiving, quantifying and measuring risk; identifying risks and estimating their importance; determining whether laws and regulations can protect us from these risks; examining how well existing laws work and how they could be improved; evaluting costs and benefits. Applications may vary by term. This course cannot be used to complete engineering unit or technical elective requirements for students in the College of Engineering.
Societal Risks and the Law: Read More [+]

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam not required.

Also listed as: POL SCI C79/STAT C79

Societal Risks and the Law: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
Development of Computer Science topics appearing in Foundations of Data Science (C8); expands computational concepts and techniques of abstraction. Understanding the structures that underlie the programs, algorithms, and languages used in data science and elsewhere. Mastery of a particular programming language while studying general techniques for managing program complexity, e.g., functional, object-oriented, and declarative programming. Provides practical experience with composing larger systems through several significant programming projects.
Computational Structures in Data Science: Read More [+]

Objectives & Outcomes

Course Objectives: Develop a foundation of computer science concepts that arise in the context of data analytics, including algorithm, representation, interpretation, abstraction, sequencing, conditional, function, iteration, recursion, types, objects, and testing, and develop proficiency in the application of these concepts in the context of a modern programming language at a scale of whole programs on par with a traditional CS introduction course.

Student Learning Outcomes: Students will be able to demonstrate a working knowledge of these concepts and a proficiency of programming based upon them sufficient to construct substantial stand-alone programs.

Rules & Requirements

Prerequisites: MATH 1A. Also, this course is a Data Science connector course and may only be taken concurrently with or after COMPSCI C8/DATA C8/INFO C8/STAT C8. Students may take more than one Data Science connector (88) course if they wish, concurrent with or after having taken the C8 course

Credit Restrictions: Students may receive no credit for Computer Science 88 after completing Computer Science 61A.

Hours & Format

Fall and/or spring: 15 weeks - 2 hours of lecture and 2 hours of laboratory per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Computational Structures in Data Science: Read Less [-]

Terms offered: Fall 2015
Topics will vary semester to semester. See the Computer Science Division announcements.
Special Topics: Read More [+]

Rules & Requirements

Prerequisites: Consent of instructor

Repeat rules: Course may be repeated for credit when topic changes.

Hours & Format

Fall and/or spring: 15 weeks - 1-4 hours of lecture per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Special Topics: Read Less [-]

Terms offered: Fall 2015, Spring 2015, Fall 2014
Students take part in organized individual field sponsored programs with off-campus companies or tutoring/mentoring relevant to specific aspects and applications of computer science on or off campus. Note Summer CPT or OPT students: written report required. Course does not count toward major requirements, but will be counted in the cumulative units toward graduation.
Field Study: Read More [+]

Rules & Requirements

Prerequisites: Consent of instructor (see department adviser)

Repeat rules: Course may be repeated for credit without restriction.

Hours & Format

Fall and/or spring: 15 weeks - 1-4 hours of fieldwork per week

Summer:
6 weeks - 2.5-10 hours of fieldwork per week
8 weeks - 2-7.5 hours of fieldwork per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Offered for pass/not pass grade only. Final exam not required.

Field Study: Read Less [-]

Terms offered: Fall 2018, Fall 2016, Fall 2015
Seminars for group study of selected topics, which will vary from year to year. Intended for students in the lower division.
Directed Group Study: Read More [+]

Rules & Requirements

Prerequisites: Consent of instructor

Repeat rules: Course may be repeated for credit without restriction.

Hours & Format

Fall and/or spring: 15 weeks - 1-4 hours of directed group study per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Offered for pass/not pass grade only. Final exam not required.

Directed Group Study: Read Less [-]

Terms offered: Fall 2015, Fall 2014, Spring 2014
A course for lower division students in good standing who wish to undertake a program of individual inquiry initiated jointly by the student and a professor. There are no other formal prerequisites, but the supervising professor must be convinced that the student is able to profit by the program.
Individual Study and Research for Undergraduates: Read More [+]

Rules & Requirements

Prerequisites: GPA of 3.4 or better

Repeat rules: Course may be repeated for credit without restriction.

Hours & Format

Fall and/or spring: 15 weeks - 0 hours of independent study per week

Summer:
6 weeks - 1-5 hours of independent study per week
8 weeks - 1-4 hours of independent study per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Offered for pass/not pass grade only. Final exam not required.

Individual Study and Research for Undergraduates: Read Less [-]

Terms offered: Fall 2020, Summer 2020 8 Week Session, Spring 2020, Fall 2019
In this course, students will explore the data science lifecycle, including question formulation, data collection and cleaning, exploratory data analysis and visualization, statistical inference and prediction, and decision-making. This class will focus on quantitative critical thinking and key principles and techniques needed to carry out this cycle. These include languages for transforming, querying and analyzing data; algorithms for machine learning methods including regression, classification and clustering; principles behind creating informative data visualizations; statistical concepts of measurement error and prediction; and techniques for scalable data processing.
Principles & Techniques of Data Science: Read More [+]

Rules & Requirements

Prerequisites: COMPSCI C8 / DATA C8 / INFO C8 / STAT C8; and COMPSCI 61A, COMPSCI 88, or ENGIN 7; Corequisite: MATH 54 or EECS 16A

Credit Restrictions: Students will receive no credit for DATA C100\STAT C100\COMPSCI C100 after completing DATA 100. A deficient grade in DATA C100\STAT C100\COMPSCI C100 may be removed by taking DATA 100.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 1 hour of laboratory per week

Summer: 8 weeks - 6 hours of lecture, 2 hours of discussion, and 2 hours of laboratory per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Formerly known as: Statistics C100/Computer Science C100

Also listed as: DATA C100/STAT C100

Principles & Techniques of Data Science: Read Less [-]

Terms offered: Spring 2015
Hardware description languages for digital system design and interactions with tool flows. Design, implementation, and verification of digital designs. Digital synthesis, partitioning, placement, routing, and simulation for Field-Programmable Gate Arrays. Large digital-system design concepts. Project design component – example, a full processor implementation with peripherals.
Programmable Digital Systems Laboratory: Read More [+]

Objectives & Outcomes

Student Learning Outcomes: This course is a one-time offering to supplement the EE141 course offered in the Fall 2014, with a lab and project section that cover the design of larger digital systems on a programmable chip platform (FPGA). The EE141 lectures in the Fall 2014 already covered the necessary lecture material, so students who took the EE141 lab in the Fall of 2014 will have a chance to expand their skills into the area of FPGA Digital System Design. Hence the pre-requisite for this course is that a student has taken the EE141 course in the Fall 2014.

Rules & Requirements

Prerequisites: COMPSCI 61C; EL ENG 105 recommended

Credit Restrictions: Students will receive no credit for Computer Science 146L after taking Fall 2014 version of Computer Science 150.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of laboratory and 1 hour of discussion per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Offered for pass/not pass grade only. Final exam not required.

Instructor: Stojanovic

Programmable Digital Systems Laboratory: Read Less [-]

Terms offered: Spring 2020, Spring 2019, Spring 2018
Instruction set architecture, microcoding, pipelining (simple and complex). Memory hierarchies and virtual memory. Processor parallelism: VLIW, vectors, multithreading. Multiprocessors.
Computer Architecture and Engineering: Read More [+]

Rules & Requirements

Prerequisites: COMPSCI 61C

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of discussion per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Alternative to final exam.

Instructors: Asanovic, Culler, Kubiatowicz, Wawrzynek

Computer Architecture and Engineering: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
The design, implementation, and evaluation of user interfaces. User-centered design and task analysis. Conceptual models and interface metaphors. Usability inspection and evaluation methods. Analysis of user study data. Input methods (keyboard, pointing, touch, tangible) and input models. Visual design principles. Interface prototyping and implementation methodologies and tools. Students will develop a user interface for a specific task and target user group in teams.
User Interface Design and Development: Read More [+]

Rules & Requirements

Prerequisites: COMPSCI 61B or COMPSCI 61BL

Credit Restrictions: Students will receive no credit for Computer Science 160 after taking Computer Science 260A.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Summer: 8 weeks - 6 hours of lecture and 2 hours of discussion per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructors: Agrawala, Canny, Hartmann, Paulos

User Interface Design and Development: Read Less [-]

Terms offered: Fall 2020, Summer 2020 8 Week Session, Spring 2020
Introduction to computer security. Cryptography, including encryption, authentication, hash functions, cryptographic protocols, and applications. Operating system security, access control. Network security, firewalls, viruses, and worms. Software security, defensive programming, and language-based security. Case studies from real-world systems.
Computer Security: Read More [+]

Rules & Requirements

Prerequisites: COMPSCI 61C; and COMPSCI 70 or MATH 55

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Summer: 8 weeks - 6 hours of lecture and 2 hours of discussion per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructors: Paxson, Song, Tygar, Wagner

Computer Security: Read Less [-]

Terms offered: Fall 2020, Summer 2020 8 Week Session, Spring 2020
Basic concepts of operating systems and system programming. Utility programs, subsystems, multiple-program systems. Processes, interprocess communication, and synchronization. Memory allocation, segmentation, paging. Loading and linking, libraries. Resource allocation, scheduling, performance evaluation. File systems, storage devices, I/O systems. Protection, security, and privacy.
Operating Systems and System Programming: Read More [+]

Rules & Requirements

Prerequisites: COMPSCI 61B, COMPSCI 61C, and COMPSCI 70

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Summer: 8 weeks - 6 hours of lecture and 2 hours of discussion per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructors: Joseph, Kubiatowicz, Stoica

Operating Systems and System Programming: Read Less [-]

Terms offered: Fall 2020, Fall 2019, Spring 2019
Survey of programming languages. The design of modern programming languages. Principles and techniques of scanning, parsing, semantic analysis, and code generation. Implementation of compilers, interpreters, and assemblers. Overview of run-time organization and error handling.
Programming Languages and Compilers: Read More [+]

Rules & Requirements

Prerequisites: COMPSCI 61B and COMPSCI 61C

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructors: Bodik, Hilfinger, Necula

Programming Languages and Compilers: Read Less [-]

Terms offered: Spring 2020, Fall 2018, Fall 2017
This course is an introduction to the Internet architecture. We will focus on the concepts and fundamental design principles that have contributed to the Internet's scalability and robustness and survey the various protocols and algorithms used within this architecture. Topics include layering, addressing, intradomain routing, interdomain routing, reliable delivery, congestion control, and the core protocols (e.g., TCP, UDP, IP, DNS, and HTTP) and network technologies (e.g., Ethernet, wireless).
Introduction to the Internet: Architecture and Protocols: Read More [+]

Rules & Requirements

Prerequisites: COMPSCI 61B and COMPSCI 162

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Summer: 8 weeks - 6 hours of lecture and 2 hours of discussion per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructors: Katz, Paxson, Ratnasamy, Shenker, Stoica

Introduction to the Internet: Architecture and Protocols: Read Less [-]

Terms offered: Fall 2019, Spring 2019, Fall 2017
Ideas and techniques for designing, developing, and modifying large software systems. Function-oriented and object-oriented modular design techniques, designing for re-use and maintainability. Specification and documentation. Verification and validation. Cost and quality metrics and estimation. Project team organization and management. Students will work in teams on a substantial programming project.
Software Engineering: Read More [+]

Rules & Requirements

Prerequisites: COMPSCI 61B and COMPSCI 61C; COMPSCI 70 or MATH 113

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Summer: 8 weeks - 6 hours of lecture and 2 hours of discussion per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructors: Brewer, Fox, Necula, Sen

Software Engineering: Read Less [-]

Terms offered: Not yet offered
Ideas and techniques for designing, developing, and modifying large software systems. Service-oriented architecture, behavior-driven design with user stories, cloud computing, test-driven development, automated testing, cost and quality metrics for maintainability and effort estimation, practical performance and security in software operations, design patterns and refactoring, specification and documentation, agile project team organization and management.
Introduction to Software Engineering: Read More [+]

Objectives & Outcomes

Student Learning Outcomes: Students will learn how to approach and add functionality to a legacy code base;
Students will learn how to identify, measure, and resolve maintainability problems in code;
Students will learn how to work with nontechnical customers and convert customer requirements into a software plan that can be effort-estimated, built, and deployed to the public cloud, including the use of behavior-driven design, user stories, and velocity;
Students will learn how to write automated tests and measure test coverage;
Students will learn practical security and performance considerations for SaaS applications.
Students will learn the architecture and machinery of software as a service; the agile/XP methodology for software development and how it compares with other methodologies, including "Plan-and-document" methodologies;
Students will learn the role of software design patterns in refactoring, and how to identify opportunities to use them;

Rules & Requirements

Prerequisites: COMPSCI 61A and COMPSCI 61B; COMPSCI 70 is recommended

Credit Restrictions: Students will receive no credit for COMPSCI 169A after completing COMPSCI 169. A deficient grade in COMPSCI 169A may be removed by taking COMPSCI 169.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Summer: 8 weeks - 6 hours of lecture and 2 hours of discussion per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructors: Fox, Sen

Introduction to Software Engineering: Read Less [-]

Terms offered: Not yet offered
Open-ended design project enhancing or creating software for real customers in an agile team setting. Teamwork coordination, effective customer meetings, pre- and post-iteration team meetings, running scrums and standups, technical communication. Contributing as a team to an open-source project; tools and workflows associated with open source collaboration, including fork-and-pull, rebase, upstream merge, continuous deployment & integration.
Software Engineering Team Project: Read More [+]

Objectives & Outcomes

Course Objectives: Students will work in a team to develop new software or enhance existing software for a customer with a real business need.

Student Learning Outcomes: Students will learn how to conduct effective meetings with nontechnical customers and work with their feedback;
Students will learn how to coordinate teamwork on developing, testing, and deploying features; and in most cases, how to approach a legacy codebase and add features to it.
Students will learn to run a small team including rotation of team roles such as product owner, scrum master, and so on;

Rules & Requirements

Prerequisites: COMPSCI 169A

Credit Restrictions: Students will receive no credit for COMPSCI 169L after completing COMPSCI 169.

Hours & Format

Fall and/or spring: 15 weeks - 1 hour of discussion per week

Summer: 8 weeks - 2 hours of discussion per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Alternative to final exam.

Instructors: Fox, Sen

Software Engineering Team Project: Read Less [-]

Terms offered: Fall 2020, Summer 2020 8 Week Session
This course presents ideas and techniques for designing, developing, and modifying large software systems using Agile techniques and tools. Topics include: function-oriented and object-oriented modular design techniques, designing for re-use and maintainability including proper use of design patterns, behavior-driven design, test-driven development, user stories for requirements elicitation & documentation, verification and validation, cost and quality metrics and estimation, project team organization and management, analyzing and refactoring legacy code.
Software Engineering: Read More [+]

Objectives & Outcomes

Student Learning Outcomes: Students will learn how to apply BDD & TDD to identify the main parts of a legacy code base, measure code quality, and refactor code to improve its quality;
Students will learn how to apply behavior-driven development (BDD) to elicit customer needs and express them as user stories that will drive development;
Students will learn how to apply the key ideas of learning a new framework to construct and deploy simple Rails applications;
Students will learn how to apply the key ideas of learning a new language in order to construct programs in Ruby;
Students will learn how to construct unit- and module-level tests and measure their coverage;
Students will learn how to exercise best practices in planning, effort estimation, and coordination of the efforts of small software teams, using appropriate tools to support those practices;
Students will learn how to identify and repair potential app-level security and performance problems.
Students will learn how to recognize when an appropriate Design Pattern may improve code quality, and refactor code to apply those Design Patterns;
Students will learn how to summarize the key architectural elements of RESTful SaaS applications and microservices;
Students will learn to articulate the primary differences between Agile and Plan-and-Document methodologies;

Rules & Requirements

Prerequisites: COMPSCI 61A and COMPSCI 61B

Credit Restrictions: Students will receive no credit for COMPSCI W169A after completing COMPSCI 169, or COMPSCI 169A. A deficient grade in COMPSCI W169A may be removed by taking COMPSCI 169, or COMPSCI 169A.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of web-based lecture and 1 hour of discussion per week

Summer: 8 weeks - 6 hours of web-based lecture and 0 hours of discussion per week

Online: This is an online course.

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructors: Fox, Sen

Software Engineering: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
Concept and basic techniques in the design and analysis of algorithms; models of computation; lower bounds; algorithms for optimum search trees, balanced trees and UNION-FIND algorithms; numerical and algebraic algorithms; combinatorial algorithms. Turing machines, how to count steps, deterministic and nondeterministic Turing machines, NP-completeness. Unsolvable and intractable problems.
Efficient Algorithms and Intractable Problems: Read More [+]

Rules & Requirements

Prerequisites: COMPSCI 61B and COMPSCI 70

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Summer: 8 weeks - 6 hours of lecture and 2 hours of discussion per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructors: Demmel, Papadimitriou, Rao, Wagner, Vazirani

Efficient Algorithms and Intractable Problems: Read Less [-]

Terms offered: Not yet offered
Cryptography or cryptology is the science of designing algorithms and protocols for enabling parties to communicate and compute securely in an untrusted environment (e.g. secure communication, digital signature, etc.) Over the last four decades, cryptography has transformed from an ad hoc collection of mysterious tricks into a rigorous science based on firm complexity-theoretic foundations. This modern complexity-theoretic approach to cryptography will be the focus. E.g., in the context of encryption we will begin by giving a precise mathematical definition for what it means to be a secure encryption scheme and then give a construction (realizing this security notion) assuming various computational hardness assumptions (e.g. factoring).
Cryptography: Read More [+]

Rules & Requirements

Prerequisites: COMPSCI 70

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Summer: 8 weeks - 6 hours of lecture and 2 hours of discussion per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructor: Garg

Cryptography: Read Less [-]

Terms offered: Spring 2019, Spring 2016, Fall 2015
Finite automata, Turing machines and RAMs. Undecidable, exponential, and polynomial-time problems. Polynomial-time equivalence of all reasonable models of computation. Nondeterministic Turing machines. Theory of NP-completeness: Cook's theorem, NP-completeness of basic problems. Selected topics in language theory, complexity and randomness.
Computability and Complexity: Read More [+]

Rules & Requirements

Prerequisites: COMPSCI 170

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructors: Papadimitriou, Seshia, Sinclair, Vazirani

Computability and Complexity: Read Less [-]

Terms offered: Fall 2019, Spring 2019, Spring 2018
Permutations, combinations, principle of inclusion and exclusion, generating functions, Ramsey theory. Expectation and variance, Chebychev's inequality, Chernov bounds. Birthday paradox, coupon collector's problem, Markov chains and entropy computations, universal hashing, random number generation, random graphs and probabilistic existence bounds.
Combinatorics and Discrete Probability: Read More [+]

Rules & Requirements

Prerequisites: COMPSCI 170

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructors: Bartlett, Papadimitriou, Sinclair, Vazirani

Combinatorics and Discrete Probability: Read Less [-]

Terms offered: Fall 2020, Fall 2018, Fall 2017
Algorithms and probabilistic models that arise in various computational biology applications: suffix trees, suffix arrays, pattern matching, repeat finding, sequence alignment, phylogenetics, genome rearrangements, hidden Markov models, gene finding, motif finding, stochastic context free grammars, RNA secondary structure. There are no biology prerequisites for this course, but a strong quantitative background will be essential.
Algorithms for Computational Biology: Read More [+]

Rules & Requirements

Prerequisites: COMPSCI 70 and COMPSCI 170; experience programming in a language such as C, C++, Java, or Python

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructor: Song

Algorithms for Computational Biology: Read Less [-]

Terms offered: Spring 2019
Deep Networks have revolutionized computer vision, language technology, robotics and control. They have growing impact in many other areas of science and engineering. They do not however, follow a closed or compact set of theoretical principles. In Yann Lecun's words they require "an interplay between intuitive insights, theoretical modeling,
practical implementations, empirical studies, and scientific analyses." This course attempts to cover that ground.
Designing, Visualizing and Understanding Deep Neural Networks: Read More [+]

Objectives & Outcomes

Student Learning Outcomes: Students will come to understand visualizing deep networks. Exploring the training and use of deep networks with visualization tools.
Students will learn design principles and best practices: design motifs that work well in particular domains, structure optimization and parameter optimization.
Understanding deep networks. Methods with formal guarantees: generative and adversarial models, tensor factorization.

Rules & Requirements

Prerequisites: MATH 53, MATH 54, and COMPSCI 61B; COMPSCI 70 or STAT 134; COMPSCI 189 is recommended

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Alternative to final exam.

Instructor: Canny

Designing, Visualizing and Understanding Deep Neural Networks: Read Less [-]

Terms offered: Spring 2020
Deep Networks have revolutionized computer vision, language technology, robotics and control. They have growing impact in many other areas of science and engineering. They do not however, follow a closed or compact set of theoretical principles. In Yann Lecun's words they require "an interplay between intuitive insights, theoretical modeling, practical implementations, empirical studies, and scientific analyses." This course attempts to cover that ground.
Designing, Visualizing and Understanding Deep Neural Networks: Read More [+]

Objectives & Outcomes

Rules & Requirements

Prerequisites: Math 53 and Math 54 or equivalent; Computer Science 70 or Statistics 134 or Electrical Engineering and Computer Sciences 126; Computer Science 61B or equivalent; Computer Science 189 (recommended)

Credit Restrictions: Students will receive no credit for COMPSCI L182 after completing COMPSCI 182, or COMPSCI W182. A deficient grade in COMPSCI L182 may be removed by taking COMPSCI 182, or COMPSCI W182.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Alternative to final exam.

Instructor: Canny

Designing, Visualizing and Understanding Deep Neural Networks: Read Less [-]

Terms offered: Spring 2020
Deep Networks have revolutionized computer vision, language technology, robotics and control. They have growing impact in many other areas of science and engineering. They do not however, follow a closed or compact set of theoretical principles. In Yann Lecun's words they require "an interplay between intuitive insights, theoretical modeling,
practical implementations, empirical studies, and scientific analyses." This course attempts to cover that ground.
Designing, Visualizing and Understanding Deep Neural Networks: Read More [+]

Objectives & Outcomes

Rules & Requirements

Credit Restrictions: Students will receive no credit for COMPSCI W182 after completing COMPSCI 182, or COMPSCI L182. A deficient grade in COMPSCI W182 may be removed by taking COMPSCI 182, or COMPSCI L182.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of web-based lecture and 1 hour of discussion per week

Online: This is an online course.

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Alternative to final exam.

Instructor: Canny

Designing, Visualizing and Understanding Deep Neural Networks: Read Less [-]

Terms offered: Summer 2020 8 Week Session, Spring 2020, Spring 2019
Techniques of modeling objects for the purpose of computer rendering: boundary representations, constructive solids geometry, hierarchical scene descriptions. Mathematical techniques for curve and surface representation. Basic elements of a computer graphics rendering pipeline; architecture of modern graphics display devices. Geometrical transformations such as rotation, scaling, translation, and their matrix representations. Homogeneous coordinates, projective and perspective transformations. Algorithms for clipping, hidden surface removal, rasterization, and anti-aliasing. Scan-line based and ray-based rendering algorithms. Lighting models for reflection, refraction, transparency.
Foundations of Computer Graphics: Read More [+]

Rules & Requirements

Prerequisites: COMPSCI 61B or COMPSCI 61BL; programming skills in C, C++, or Java; linear algebra and calculus

Credit Restrictions: Students will receive no credit for Comp Sci 184 after taking Comp Sci 284A.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Summer: 8 weeks - 6 hours of lecture and 2 hours of discussion per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructors: O'Brien, Ng

Foundations of Computer Graphics: Read Less [-]

Terms offered: Fall 2020, Fall 2018, Spring 2018
Access methods and file systems to facilitate data access. Hierarchical, network, relational, and object-oriented data models. Query languages for models. Embedding query languages in programming languages. Database services including protection, integrity control, and alternative views of data. High-level interfaces including application generators, browsers, and report writers. Introduction to transaction processing. Database system implementation to be done as term project.
Introduction to Database Systems: Read More [+]

Rules & Requirements

Prerequisites: COMPSCI 61B and COMPSCI 61C

Credit Restrictions: Students will receive no credit for Comp Sci 186 after taking Comp Sci 286A.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructors: Franklin, Hellerstein

Introduction to Database Systems: Read Less [-]

Terms offered: Spring 2020, Fall 2019, Spring 2019
Broad introduction to systems for storing, querying, updating and managing large databases. Computer science skills synthesizing viewpoints from low-level systems architecture to high-level modeling and declarative logic. System internals, including the complex details of query optimization and execution, concurrency control, indexing, and memory management. More abstract issues in query languages and data modeling – students are exposed to formal relational languages, SQL, full-text search, entity-relationship modeling, normalization, and physical database design. Recent technological trends in the field, including “Big Data” programming libraries like MapReduce, and distributed key-value stores with various consistency models.
Introduction to Database Systems: Read More [+]

Rules & Requirements

Prerequisites: COMPSCI 61B and COMPSCI 61C

Credit Restrictions: Students will receive no credit for Computer Science W186 after taking either Computer Science 186 or Computer Science 286A.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of web-based lecture and 1 hour of discussion per week

Summer: 8 weeks - 6 hours of web-based lecture and 2 hours of discussion per week

Online: This is an online course.

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructor: Hellerstein

Introduction to Database Systems: Read Less [-]

Terms offered: Fall 2020, Summer 2020 8 Week Session, Spring 2020
Ideas and techniques underlying the design of intelligent computer systems. Topics include search, game playing, knowledge representation, inference, planning, reasoning under uncertainty, machine learning, robotics, perception, and language understanding.
Introduction to Artificial Intelligence: Read More [+]

Rules & Requirements

Prerequisites: COMPSCI 61A, COMPSCI 61B, and COMPSCI 70

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Summer: 8 weeks - 6 hours of lecture and 2 hours of discussion per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructors: Abbeel, Klein, Russell

Introduction to Artificial Intelligence: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications.
Introduction to Machine Learning: Read More [+]

Rules & Requirements

Prerequisites: MATH 53 and MATH 54; and COMPSCI 70 or consent of instructor

Credit Restrictions: Students will receive no credit for Comp Sci 189 after taking Comp Sci 289A.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Summer: 8 weeks - 6 hours of lecture and 2 hours of discussion per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructors: Abbeel, Bartlett, Darrell, El Ghaoui, Jordan, Klein, Malik, Russell

Introduction to Machine Learning: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Spring 2019
This multidisciplinary course provides an introduction to fundamental conceptual aspects of quantum mechanics from a computational and informational theoretic perspective, as well as physical implementations and technological applications of quantum information science. Basic sections of quantum algorithms, complexity, and cryptography, will be touched upon, as well as pertinent physical realizations from nanoscale science and engineering.
Quantum Information Science and Technology: Read More [+]

Rules & Requirements

Prerequisites: Linear Algebra (EECS 16A or PHYSICS 89 or MATH 54) AND either discrete mathematics (COMPSCI 70 or MATH 55), or quantum mechanics (PHYSICS 7C or PHYSICS 137A or CHEM 120A)

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Also listed as: CHEM C191/PHYSICS C191

Quantum Information Science and Technology: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
Topics will vary semester to semester. See the Computer Science Division announcements.
Special Topics: Read More [+]

Rules & Requirements

Prerequisites: Consent of instructor

Repeat rules: Course may be repeated for credit when topic changes.

Hours & Format

Fall and/or spring: 15 weeks - 1-4 hours of lecture per week

Summer: 8 weeks - 2-8 hours of lecture per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Special Topics: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
Topics include electronic community; the changing nature of work; technological risks; the information economy; intellectual property; privacy; artificial intelligence and the sense of self; pornography and censorship; professional ethics. Students will lead discussions on additional topics.
Social Implications of Computer Technology: Read More [+]

Rules & Requirements

Credit Restrictions: Students will receive no credit for 195 after taking C195/Interdisciplinary Field Study C155 or H195.

Hours & Format

Fall and/or spring: 15 weeks - 1.5 hours of lecture per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Offered for pass/not pass grade only. Final exam not required.

Instructor: Harvey

Social Implications of Computer Technology: Read Less [-]

Terms offered: Fall 2020, Spring 2014, Fall 2013
Topics include electronic community; the changing nature of work; technological risks; the information economy; intellectual property; privacy; artificial intelligence and the sense of self; pornography and censorship; professional ethics. Students may lead discussions on additional topics.
Honors Social Implications of Computer Technology: Read More [+]

Rules & Requirements

Credit Restrictions: Student will receive no credit for H195 after taking 195 or C195.

Hours & Format

Fall and/or spring: 15 weeks - 1.5 hours of lecture and 1.5 hours of discussion per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Offered for pass/not pass grade only. Final exam not required.

Instructor: Harvey

Honors Social Implications of Computer Technology: Read Less [-]

Terms offered: Fall 2016, Fall 2010, Spring 2010
Thesis work under the supervision of a faculty member. To obtain credit the student must, at the end of two semesters, submit a satisfactory thesis to the Electrical Engineering and Computer Science department archive. A total of four units must be taken. The units many be distributed between one or two semesters in any way. H196A-H196B count as graded technical elective units, but may not be used to satisfy the requirement for 27 upper division technical units in the College of Letters and Science with a major in Computer Science.
Senior Honors Thesis Research: Read More [+]

Rules & Requirements

Prerequisites: Open only to students in the computer science honors program

Hours & Format

Fall and/or spring: 15 weeks - 1-4 hours of independent study per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Senior Honors Thesis Research: Read Less [-]

Terms offered: Spring 2010, Spring 2009, Fall 2008
Thesis work under the supervision of a faculty member. To obtain credit the student must, at the end of two semesters, submit a satisfactory thesis to the Electrical Engineering and Computer Science department archive. A total of four units must be taken. The units many be distributed between one or two semesters in any way. H196A-H196B count as graded technical elective units, but may not be used to satisfy the requirement for 27 upper division technical units in the College of Letters and Science with a major in Computer Science.
Senior Honors Thesis Research: Read More [+]

Rules & Requirements

Prerequisites: Open only to students in the computer science honors program

Hours & Format

Fall and/or spring: 15 weeks - 1-4 hours of independent study per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Senior Honors Thesis Research: Read Less [-]

Terms offered: Spring 2019, Fall 2018, Fall 2016
Students take part in organized individual field sponsored programs with off-campus companies or tutoring/mentoring relevant to specific aspects and applications of computer science on or off campus. Note Summer CPT or OPT students: written report required. Course does not count toward major requirements, but will be counted in the cumulative units toward graduation.
Field Study: Read More [+]

Rules & Requirements

Prerequisites: Consent of instructor (see department adviser)

Repeat rules: Course may be repeated for credit without restriction.

Hours & Format

Fall and/or spring: 15 weeks - 1-4 hours of fieldwork per week

Summer:
6 weeks - 2.5-10 hours of fieldwork per week
8 weeks - 2-7.5 hours of fieldwork per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Offered for pass/not pass grade only. Final exam not required.

Field Study: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
Group study of selected topics in Computer Sciences, usually relating to new developments.
Directed Group Studies for Advanced Undergraduates: Read More [+]

Rules & Requirements

Prerequisites: 2.0 GPA or better; 60 units completed

Repeat rules: Course may be repeated for credit without restriction.

Hours & Format

Fall and/or spring: 15 weeks - 1-4 hours of directed group study per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Offered for pass/not pass grade only. Final exam not required.

Directed Group Studies for Advanced Undergraduates: Read Less [-]

Terms offered: Spring 2020, Fall 2018, Fall 2016
Supervised independent study. Enrollment restrictions apply.
Supervised Independent Study: Read More [+]

Rules & Requirements

Prerequisites: Consent of instructor and major adviser

Credit Restrictions: Enrollment is restricted; see the Introduction to Courses and Curricula section of this catalog.

Repeat rules: Course may be repeated for credit without restriction.

Hours & Format

Fall and/or spring: 15 weeks - 0 hours of independent study per week

Summer:
6 weeks - 1-5 hours of independent study per week
8 weeks - 1-4 hours of independent study per week

Additional Details

Subject/Course Level: Computer Science/Undergraduate

Grading/Final exam status: Offered for pass/not pass grade only. Final exam not required.

Supervised Independent Study: Read Less [-]

Electrical Engineering

Terms offered: Fall 2017, Spring 2017, Spring 2016
The Freshman Seminar Program has been designed to provide new students with the opportunity to explore an intellectual topic with a faculty member in a small seminar setting. Freshman seminars are offered in all campus departments, and topics may vary from department to department and semester to semester.
Freshman Seminar: Read More [+]

Rules & Requirements

Repeat rules: Course may be repeated for credit without restriction.

Hours & Format

Fall and/or spring: 15 weeks - 1 hour of seminar per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: The grading option will be decided by the instructor when the class is offered. Final exam required.

Freshman Seminar: Read Less [-]

Terms offered: Fall 2011
A Berkeley Electrical Engineering and Computer Sciences degree opens the door to many opportunities, but what exactly are they? Graduation is only a few years away and it's not too early to find out. In this seminar students will hear from practicing engineers who recently graduated. What are they working on? Are they working in a team? What do they wish they had learned better? How did they find their jobs?
What Electrical Engineers Do--Feedback from Recent Graduates: Read More [+]

Hours & Format

Fall and/or spring: 15 weeks - 1 hour of lecture per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Offered for pass/not pass grade only. Final exam not required.

Instructor: Boser

What Electrical Engineers Do--Feedback from Recent Graduates: Read Less [-]

Terms offered: Fall 2019, Fall 2018, Fall 2017
Freshman and sophomore seminars offer lower division students the opportunity to explore an intellectual topic with a faculty member and a group of peers in a small-seminar setting. These seminars are offered in all campus departments; topics vary from department to department and from semester to semester. Enrollment limits are set by the faculty, but the suggested limit is 25.
Freshman/Sophomore Seminar: Read More [+]

Rules & Requirements

Prerequisites: Priority given to freshmen and sophomores

Repeat rules: Course may be repeated for credit when topic changes.

Hours & Format

Fall and/or spring: 15 weeks - 2-4 hours of seminar per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: The grading option will be decided by the instructor when the class is offered. Final exam required.

Freshman/Sophomore Seminar: Read Less [-]

Terms offered: Fall 2013, Summer 2013 8 Week Session, Spring 2013
This course serves as an introduction to the principles of electrical engineering, starting from the basic concepts of voltage and current and circuit elements of resistors, capacitors, and inductors. Circuit analysis is taught using Kirchhoff's voltage and current laws with Thevenin and Norton equivalents. Operational amplifiers with feedback are introduced as basic building blocks for amplication and filtering. Semiconductor devices including diodes and MOSFETS and their IV characteristics are covered. Applications of diodes for rectification, and design of MOSFETs in common source amplifiers are taught. Digital logic gates and design using CMOS as well as simple flip-flops are introduced. Speed and scaling issues for CMOS are considered. The course includes as motivating examples designs of high level applications including logic circuits, amplifiers, power supplies, and communication links.
Introduction to Digital Electronics: Read More [+]

Rules & Requirements

Prerequisites: Mathematics 1B

Credit Restrictions: Students will receive no credit for 42 after taking 40 or 100.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Summer: 8 weeks - 6 hours of lecture and 2 hours of discussion per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Introduction to Digital Electronics: Read Less [-]

Terms offered: Spring 2020, Spring 2019, Fall 2018
Electronics has become pervasive in our lives as a powerful technology with applications in a wide range of fields including healthcare, environmental monitoring, robotics, or entertainment. This course teaches how to build electronic circuits that interact with the environment through sensors and actuators and how to communicate wirelessly with the internet to cooperate with other devices and with humans. In the laboratory students design and build representative samples such as solar harvesters, robots, that exchange information with or are controlled from the cloud.
Electronics for the Internet of Things: Read More [+]

Objectives & Outcomes

Course Objectives: Electronics has become a powerful and ubiquitous technology supporting solutions to a wide range of applications in fields ranging from science, engineering, healthcare, environmental monitoring, transportation, to entertainment. The objective of this course is to teach students majoring in these and related subjects how to use electronic devices to solve problems in their areas of expertise.
Through the lecture and laboratory, students gain insight into the possibilities and limitations of the technology and how to use electronics to help solve problems. Students learn to use electronics to interact with the environment through sound, light, temperature, motion using sensors and actuators, and how to use electronic computation to orchestrate the interactions and exchange information wirelessly over the internet.

Student Learning Outcomes: Deploy electronic sensors and interface them to microcontrollers through digital and analog channels as well as common protocols (I2C, SPI),
Design, build and test electronic devices leveraging these concepts.
Interact with the internet and cloud services using protocols such as http, MQTT, Blynk,
Interface DC motors, steppers and servos to microcontrollers,
Represent information with voltage, current, power, and energy and how to measure these quantities with laboratory equipment,
To use and program low-cost and low-power microcontrollers for sensing, actuation, and information processing, and find and use program libraries supporting these tasks
Understand and make basic low-pass and high-pass filters, Wheatstone bridge etc.
Use electronics to sense and actuate physical parameters such as temperature, humidity, sound, light, and motion,

Rules & Requirements

Prerequisites: ENGIN 7, COMPSCI 10, or equivalent background in computer programming (including COMPSCI 61A or COMPSCI C8 / INFO C8 / STAT C8); MATH 1A or equivalent background in Calculus

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture, 2 hours of discussion, and 3 hours of laboratory per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructor: Boser

Electronics for the Internet of Things: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
Sophomore seminars are small interactive courses offered by faculty members in departments all across the campus. Sophomore seminars offer opportunity for close, regular intellectual contact between faculty members and students in the crucial second year. The topics vary from department to department and semester to semester. Enrollment limited to 15 sophomores.
Sophomore Seminar: Read More [+]

Rules & Requirements

Prerequisites: At discretion of instructor

Repeat rules: Course may be repeated for credit when topic changes.

Hours & Format

Fall and/or spring:
5 weeks - 3-6 hours of seminar per week
10 weeks - 1.5-3 hours of seminar per week
15 weeks - 1-2 hours of seminar per week

Summer:
6 weeks - 2.5-5 hours of seminar per week
8 weeks - 1.5-3.5 hours of seminar per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: The grading option will be decided by the instructor when the class is offered. Final exam required.

Sophomore Seminar: Read Less [-]

Terms offered: Spring 2016, Fall 2015, Spring 2015
Students take part in organized individual field sponsored programs with off-campus companies or tutoring/mentoring relevant to specific aspects and applications of computer science on or off campus. Note Summer CPT or OPT students: written report required. Course does not count toward major requirements, but will be counted in the cumulative units toward graduation.
Field Study: Read More [+]

Rules & Requirements

Prerequisites: Consent of instructor (see department adviser)

Repeat rules: Course may be repeated for credit without restriction.

Hours & Format

Fall and/or spring: 15 weeks - 1-4 hours of fieldwork per week

Summer:
6 weeks - 2.5-10 hours of fieldwork per week
8 weeks - 2-7.5 hours of fieldwork per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Offered for pass/not pass grade only. Final exam not required.

Field Study: Read Less [-]

Terms offered: Fall 2020, Fall 2016, Spring 2016
Group study of selected topics in electrical engineering, usually relating to new developments.
Directed Group Study for Undergraduates: Read More [+]

Rules & Requirements

Repeat rules: Course may be repeated for credit without restriction.

Hours & Format

Fall and/or spring: 15 weeks - 1-4 hours of directed group study per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Offered for pass/not pass grade only. Final exam not required.

Directed Group Study for Undergraduates: Read Less [-]

Terms offered: Spring 2016, Fall 2015, Spring 2015
Supervised independent study and research for students with fewer than 60 units completed.
Individual Study and Research for Undergraduates: Read More [+]

Rules & Requirements

Prerequisites: Freshman or sophomore standing and consent of instructor. Minimum GPA of 3.4 required

Credit Restrictions: Enrollment is restricted; see the Introduction to Courses and Curricula section of this catalog.

Repeat rules: Course may be repeated for credit without restriction.

Hours & Format

Fall and/or spring: 15 weeks - 1-4 hours of independent study per week

Summer:
6 weeks - 1-5 hours of independent study per week
8 weeks - 1-4 hours of independent study per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Offered for pass/not pass grade only. Final exam not required.

Individual Study and Research for Undergraduates: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
This course covers the fundamental circuit and device concepts needed to understand analog integrated circuits. After an overview of the basic properties of semiconductors, the p-n junction and MOS capacitors are described and the MOSFET is modeled as a large-signal device. Two port small-signal amplifiers and their realization using single stage and multistage CMOS building blocks are discussed. Sinusoidal steady-state signals are introduced and the techniques of phasor analysis are developed, including impedance and the magnitude and phase response of linear circuits. The frequency responses of single and multi-stage amplifiers are analyzed. Differential amplifiers are introduced.
Microelectronic Devices and Circuits: Read More [+]

Rules & Requirements

Prerequisites: EECS 16A and EECS 16B

Credit Restrictions: Students will receive no credit for EL ENG 105 after completing EL ENG 240A, or EL ENG 140.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 3 hours of laboratory per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Microelectronic Devices and Circuits: Read Less [-]

Terms offered: Fall 2017, Fall 2016, Fall 2015
An introduction to the kinematics, dynamics, and control of robot manipulators, robotic vision, and sensing. The course covers forward and inverse kinematics of serial chain manipulators, the manipulator Jacobian, force relations, dynamics, and control. It presents elementary principles on proximity, tactile, and force sensing, vision sensors, camera calibration, stereo construction, and motion detection. The course concludes with current applications of robotics in active perception, medical robotics, and other areas.
Introduction to Robotics: Read More [+]

Rules & Requirements

Prerequisites: EL ENG 120 or consent of instructor

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 3 hours of laboratory per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructor: Bajcsy

Formerly known as: Electrical Engineering C125/Bioengineering C125

Also listed as: BIO ENG C125

Introduction to Robotics: Read Less [-]

Terms offered: Spring 2017, Spring 2016
This course is a sequel to Electrical Engineering C106A/Bioengineering C125, which covers kinematics, dynamics and control of a single robot. This course will cover dynamics and control of groups of robotic manipulators coordinating with each other and interacting with the environment. Concepts will include an introduction to grasping and the constrained manipulation, contacts and force control for interaction with the environment. We will also cover active perception guided manipulation, as well as the manipulation of non-rigid objects. Throughout, we will emphasize design and human-robot interactions, and applications to applications in manufacturing, service robotics, tele-surgery, and locomotion.
Robotic Manipulation and Interaction: Read More [+]

Rules & Requirements

Prerequisites: EECS C106A / BIO ENG C125 or consent of the instructor

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 3 hours of laboratory per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Letter grade. Alternative to final exam.

Instructors: Bajcsy, Sastry

Also listed as: BIO ENG C125B

Robotic Manipulation and Interaction: Read Less [-]

Terms offered: Fall 2020, Fall 2019, Spring 2019
Power conversion circuits and techniques. Characterization and design of magnetic devices including transformers, reactors, and electromagnetic machinery. Characteristics of bipolar and MOS power semiconductor devices. Applications to motor control, switching power supplies, lighting, power systems, and other areas as appropriate.
Power Electronics: Read More [+]

Rules & Requirements

Prerequisites: EL ENG 105 or consent of instructor

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 3 hours of laboratory per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Power Electronics: Read Less [-]

Terms offered: Spring 2020, Spring 2019, Spring 2018
Review of static electric and magnetic fields and applications; Maxwell's equations; transmission lines; propagation and reflection of plane waves; introduction to guided waves, microwave networks, and radiation and antennas. Minilabs on statics, transmission lines, and waves. Explanation of cellphone antennas, WiFi communication, and other wireless technologies.
Electromagnetic Fields and Waves: Read More [+]

Rules & Requirements

Prerequisites: EECS 16B, MATH 53, and MATH 54; PHYSICS 7B or equivalent that covers AC circuits and electromagnetics up to Maxwell's equations

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 2 hours of laboratory per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructor: Yablonovitch

Electromagnetic Fields and Waves: Read Less [-]

Terms offered: Fall 2020, Fall 2019, Spring 2019
Fundamental principles of optical systems. Geometrical optics and aberration theory. Stops and apertures, prisms, and mirrors. Diffraction and interference. Optical materials and coatings. Radiometry and photometry. Basic optical devices and the human eye. The design of optical systems. Lasers, fiber optics, and holography.
Introduction to Optical Engineering: Read More [+]

Rules & Requirements

Prerequisites: MATH 53; EECS 16A and EECS 16B, or MATH 54

Credit Restrictions: Students will receive no credit for Electrical Engineering 118 after taking Electrical Engineering 218A. A deficient grade in Electrical Engineering 119 may be removed by taking Electrical Engineering 118.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructors: Waller, Kante

Introduction to Optical Engineering: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
Continuous and discrete-time transform analysis techniques with illustrative applications. Linear and time-invariant systems, transfer functions. Fourier series, Fourier transform, Laplace and Z-transforms. Sampling and reconstruction. Solution of differential and difference equations using transforms. Frequency response, Bode plots, stability analysis. Illustrated by analysis of communication systems and feedback control systems.
Signals and Systems: Read More [+]

Rules & Requirements

Prerequisites: EECS 16A and EECS 16B

Hours & Format

Fall and/or spring: 15 weeks - 4 hours of lecture and 1 hour of recitation per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Signals and Systems: Read Less [-]

Terms offered: Spring 2016, Fall 2014, Fall 2013
Introduction to the basic principles of the design and analysis of modern digital communication systems. Topics include source coding, channel coding, baseband and passband modulation techniques, receiver design, and channel equalization. Applications to design of digital telephone modems, compact disks, and digital wireless communication systems. Concepts illustrated by a sequence of MATLAB exercises.
Introduction to Digital Communication Systems: Read More [+]

Rules & Requirements

Prerequisites: EECS 16A, EECS 16B, and COMPSCI 70

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Introduction to Digital Communication Systems: Read Less [-]

Terms offered: Spring 2020, Spring 2019, Spring 2018
This course focuses on the fundamentals of the wired and wireless communication networks. The course covers both the architectural principles for making these networks scalable and robust, as well as the key techniques essential for analyzing and designing them. The topics include graph theory, Markov chains, queuing, optimization techniques, the physical and link layers, switching, transport, cellular networks and Wi-Fi.
Introduction to Communication Networks: Read More [+]

Rules & Requirements

Prerequisites: COMPSCI 70

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Introduction to Communication Networks: Read Less [-]

Terms offered: Spring 2020, Spring 2019, Spring 2018
Discrete time signals and systems: Fourier and Z transforms, DFT, 2-dimensional versions. Digital signal processing topics: flow graphs, realizations, FFT, chirp-Z algorithms, Hilbert transform relations, quantization effects, linear prediction. Digital filter design methods: windowing, frequency sampling, S-to-Z methods, frequency-transformation methods, optimization methods, 2-dimensional filter design.
Digital Signal Processing: Read More [+]

Rules & Requirements

Prerequisites: EL ENG 120

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 1 hour of laboratory per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Digital Signal Processing: Read Less [-]

Terms offered: Spring 2017, Fall 2016, Spring 2016
This course covers the fundamentals of probability and random processes useful in fields such as networks, communication, signal processing, and control. Sample space, events, probability law. Conditional probability. Independence. Random variables. Distribution, density functions. Random vectors. Law of large numbers. Central limit theorem. Estimation and detection. Markov chains.
Probability and Random Processes: Read More [+]

Rules & Requirements

Prerequisites: EECS 16A and EECS 16B

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Probability and Random Processes: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
Analysis and synthesis of linear feedback control systems in transform and time domains. Control system design by root locus, frequency response, and state space methods. Applications to electro-mechanical and mechatronics systems.
Feedback Control Systems: Read More [+]

Rules & Requirements

Prerequisites: EECS 16A or MEC ENG 100; MEC ENG 132 or EL ENG 120

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 3 hours of laboratory per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Also listed as: MEC ENG C134

Feedback Control Systems: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
Overview of electronic properties of semiconductor. Metal-semiconductor contacts, pn junctions, bipolar transistors, and MOS field-effect transistors. Properties that are significant to device operation for integrated circuits. Silicon device fabrication technology.
Integrated-Circuit Devices: Read More [+]

Rules & Requirements

Prerequisites: EECS 16A and EECS 16B

Credit Restrictions: Students will receive no credit for El Eng 130 after taking El Eng 230A.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Integrated-Circuit Devices: Read Less [-]

Terms offered: Spring 2020, Spring 2019, Spring 2018
This course is designed to give an introduction to, and overview of, the fundamentals of photovoltaic devices. Students will learn how solar cells work, understand the concepts and models of solar cell device physics, and formulate and solve relevant physical problems related to photovoltaic devices. Monocrystalline, thin film and third generation solar cells will be discussed and analyzed. Light management and economic considerations in a solar cell system will also be covered.
Fundamentals of Photovoltaic Devices: Read More [+]

Rules & Requirements

Prerequisites: EECS 16A and EECS 16B

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructor: Arias

Fundamentals of Photovoltaic Devices: Read Less [-]

Terms offered: Fall 2020, Fall 2019, Fall 2018
Overview of conventional electric power conversion and delivery, emphasizing a systemic understanding of the electric grid with primary focus at the transmission level, aimed toward recognizing needs and opportunities for technological innovation. Topics include aspects of a.c. system design, electric generators, components of transmission and distribution systems, power flow analysis, system planning and operation, performance measures, and limitations of legacy technologies.
Introduction to Electric Power Systems: Read More [+]

Rules & Requirements

Prerequisites: Physics 7B; EECS 16A and EECS 16B, or consent of instructor

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructor: von Meier

Introduction to Electric Power Systems: Read Less [-]

Terms offered: Spring 2020, Spring 2019, Spring 2018
Overview of recent and potential future evolution of electric power systems with focus on new and emerging technologies for power conversion and delivery, primarily at the distribution level. Topics include power electronics applications, solar and wind generation, distribution system design and operation, electric energy storage, information management and communications, demand response, and microgrids.
Introduction to Electric Power Systems: Read More [+]

Rules & Requirements

Prerequisites: EL ENG 137A or consent of instructor

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructor: von Meier

Introduction to Electric Power Systems: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
Single and multiple stage transistor amplifiers. Operational amplifiers. Feedback amplifiers, 2-port formulation, source, load, and feedback network loading. Frequency response of cascaded amplifiers, gain-bandwidth exchange, compensation, dominant pole techniques, root locus. Supply and temperature independent biasing and references. Selected applications of analog circuits such as analog-to-digital converters, switched capacitor filters, and comparators. Hardware laboratory and design project.
Linear Integrated Circuits: Read More [+]

Rules & Requirements

Prerequisites: EL ENG 105

Credit Restrictions: Students will receive no credit for El Eng 140 after taking El Eng 240A.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 3 hours of laboratory per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructors: Alon, Sanders

Linear Integrated Circuits: Read Less [-]

Terms offered: Fall 2019, Fall 2018, Fall 2017
Analysis and design of electronic circuits for communication systems, with an emphasis on integrated circuits for wireless communication systems. Analysis of noise and distortion in amplifiers with application to radio receiver design. Power amplifier design with application to wireless radio transmitters. Radio-frequency mixers, oscillators, phase-locked loops, modulators, and demodulators.
Integrated Circuits for Communications: Read More [+]

Rules & Requirements

Prerequisites: EECS 16A, EECS 16B, and EL ENG 105

Credit Restrictions: Students will receive no credit for El Eng 142 after taking El Eng 242A.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 3 hours of laboratory per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Integrated Circuits for Communications: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
Integrated circuit device fabrication and surface micromachining technology. Thermal oxidation, ion implantation, impurity diffusion, film deposition, expitaxy, lithography, etching, contacts and interconnections, and process integration issues. Device design and mask layout, relation between physical structure and electrical/mechanical performance. MOS transistors and poly-Si surface microstructures will be fabricated in the laboratory and evaluated.
Microfabrication Technology: Read More [+]

Rules & Requirements

Prerequisites: PHYSICS 7B

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 3 hours of laboratory per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Microfabrication Technology: Read Less [-]

Terms offered: Fall 2015, Fall 2014, Fall 2013
The modeling, analysis, and optimization of complex systems requires a range of algorithms and design software. This course reviews the fundamental techniques underlying the design methodology for complex systems, using integrated circuit design as example. Topics include design flows, discrete and continuous models and algorithms, and strategies for implementing algorithms efficiently and correctly in software. Laboratory assignments and a class project will expose students to state-of-the-art tools.
Fundamental Algorithms for Systems Modeling, Analysis, and Optimization: Read More [+]

Rules & Requirements

Prerequisites: EECS 16A and COMPSCI 70, or consent of instructor

Hours & Format

Fall and/or spring: 15 weeks - 4 hours of lecture per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructors: Keutzer, Lee, Roychowdhury, Seshia

Fundamental Algorithms for Systems Modeling, Analysis, and Optimization: Read Less [-]

Terms offered: Fall 2020, Fall 2019, Fall 2018
Biomedical imaging is a clinically important application of engineering, applied mathematics, physics, and medicine. In this course, we apply linear systems theory and basic physics to analyze X-ray imaging, computerized tomography, nuclear medicine, and MRI. We cover the basic physics and instrumentation that characterizes medical image as an ideal perfect-resolution image blurred by an impulse response. This material could prepare the student for a career in designing new medical imaging systems that reliably detect small tumors or infarcts.
Medical Imaging Signals and Systems: Read More [+]

Rules & Requirements

Prerequisites: EECS 16A and EECS 16B

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructor: Conolly

Also listed as: BIO ENG C165

Medical Imaging Signals and Systems: Read Less [-]

Terms offered: Fall 2014, Fall 2013, Fall 2012
Laboratory exercises exploring a variety of electronic transducers for measuring physical quantities such as temperature, force, displacement, sound, light, ionic potential; the use of circuits for low-level differential amplification and analog signal processing; and the use of microcomputers for digital sampling and display. Lectures cover principles explored in the laboratory exercises; construction, response and signal to noise of electronic transducers and actuators; and design of circuits for sensing and controlling physical quantities.
Introductory Electronic Transducers Laboratory: Read More [+]

Hours & Format

Fall and/or spring: 15 weeks - 2 hours of lecture and 3 hours of laboratory per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructor: Derenzo

Also listed as: BIO ENG C145L

Introductory Electronic Transducers Laboratory: Read Less [-]

Terms offered: Spring 2013, Spring 2012, Spring 2011
Laboratory exercises constructing basic interfacing circuits and writing 20-100 line C programs for data acquisition, storage, analysis, display, and control. Use of the IBM PC with microprogrammable digital counter/timer, parallel I/O port. Circuit components include anti-aliasing filters, the S/H amplifier, A/D and D/A converters. Exercises include effects of aliasing in periodic sampling, fast Fourier transforms of basic waveforms, the use of the Hanning filter for leakage reduction, Fourier analysis of the human voice, digital filters, and control using Fourier deconvolution. Lectures cover principles explored in the lab exercises and design of microcomputer-based systems for data acquisitions, analysis and control.
Introductory Microcomputer Interfacing Laboratory: Read More [+]

Rules & Requirements

Prerequisites: EE 16A & 16B

Hours & Format

Fall and/or spring: 15 weeks - 2 hours of lecture and 3 hours of laboratory per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructor: Derenzo

Also listed as: BIO ENG C145M

Introductory Microcomputer Interfacing Laboratory: Read Less [-]

Terms offered: Spring 2015, Spring 2014, Spring 2013, Spring 2012
Introduction to laboratory and field study of the biomechanics of animals and plants using fundamental biomechanical techniques and equipment. Course has a series of rotations involving students in experiments demonstrating how solid and fluid mechanics can be used to discover the way in which diverse organisms move and interact with their physical environment. The laboratories emphasize sampling methodology, experimental design, and statistical interpretation of results. Latter third of course devoted to independent research projects. Written reports and class presentation of project results are required.
Laboratory in the Mechanics of Organisms: Read More [+]

Rules & Requirements

Prerequisites: Integrative Biology 135 or consent of instructor; for Electrical Engineering and Computer Science students, Electrical Engineering 105, 120 or Computer Science 184

Credit Restrictions: Students will receive no credit for C135L after taking 135L.

Hours & Format

Fall and/or spring: 15 weeks - 6 hours of laboratory, 1 hour of discussion, and 1 hour of fieldwork per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Formerly known as: Integrative Biology 135L

Also listed as: BIO ENG C136L/INTEGBI C135L

Laboratory in the Mechanics of Organisms: Read Less [-]

Terms offered: Spring 2015
This is a lab course that covers the design of modern Application-Specific Integrated Circuits (ASICs). The labs lay the foundation of modern digital design by first setting-up the scripting and hardware description language base for specification of digital systems and interactions with tool flows. Software testing of digital designs is covered leading into a set of labs that cover the design flow. Digital synthesis, floorplanning, placement and routing are covered, as well as tools to evaluate design timing and power. Chip-level assembly is covered, instantiation of custom IP blocks: I/O pads, memories, PLLs, etc. The labs culminate with a project design – implementation of a 3-stage RISC-V processor with register file and caches.
Application Specific Integrated Circuits Laboratory: Read More [+]

Objectives & Outcomes

Course Objectives: This course is a one-time offering to supplement the CS150 course offered in the Fall 2014, with a lab and project section that cover the Application-Specific Integrated Circuit Design. The CS150 lectures in the Fall 2014 already covered the necessary lecture material, so students who took the CS150 lab in the Fall of 2014 will have a chance to expand their skills into the area of Application-Specific Integrated Circuit design.
Hence the pre-requisite for this course is that a student has taken the CS150 course in the Fall 2014.

Rules & Requirements

Prerequisites: EECS 16B; EL ENG 105 recommended

Credit Restrictions: Students will receive no credit for Electrical Engineering 146L after taking Fall 2014 version of Electrical Engineering 141/241A.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of laboratory and 1 hour of discussion per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Offered for pass/not pass grade only. Final exam not required.

Instructor: Stojanovic

Application Specific Integrated Circuits Laboratory: Read Less [-]

Terms offered: Fall 2020, Fall 2019, Fall 2018
This course will teach fundamentals of micromachining and microfabrication techniques, including planar thin-film process technologies, photolithographic techniques, deposition and etching techniques, and the other technologies that are central to MEMS fabrication. It will pay special attention to teaching of fundamentals necessary for the design and analysis of devices and systems in mechanical, electrical, fluidic, and thermal energy/signal domains, and will teach basic techniques for multi-domain analysis. Fundamentals of sensing and transduction mechanisms including capacitive and piezoresistive techniques, and design and analysis of micmicromachined miniature sensors and actuators using these techniques will be covered.
Introduction to Microelectromechanical Systems (MEMS): Read More [+]

Rules & Requirements

Prerequisites: EECS 16A and EECS 16B

Credit Restrictions: Students will receive no credit for El Eng 147 after taking El Eng 247A.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructors: Maharbiz, Nguyen, Pister

Introduction to Microelectromechanical Systems (MEMS): Read Less [-]

Terms offered: Spring 2020, Spring 2019, Spring 2018
Design project course, focusing on application of theoretical principles in electrical engineering to control of a small-scale system, such as a mobile robot. Small teams of students will design and construct a mechatronic system incorporating sensors, actuators, and intelligence.
Mechatronic Design Laboratory: Read More [+]

Rules & Requirements

Prerequisites: EECS 16A, EECS 16B, COMPSCI 61A, COMPSCI 61B, COMPSCI 61C, and EL ENG 120

Hours & Format

Fall and/or spring: 15 weeks - 1.5 hours of lecture and 10 hours of laboratory per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Instructor: Fearing

Mechatronic Design Laboratory: Read Less [-]

Terms offered: Spring 2020, Fall 2018, Spring 2018
Topics will vary semester to semester. See the Electrical Engineering announcements.
Special Topics: Read More [+]

Rules & Requirements

Prerequisites: Consent of instructor

Repeat rules: Course may be repeated for credit when topic changes.

Hours & Format

Fall and/or spring: 15 weeks - 1-4 hours of lecture per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Letter grade. Final exam required.

Special Topics: Read Less [-]

Terms offered: Spring 2016, Fall 2015, Spring 2015
Thesis work under the supervision of a faculty member. A minimum of four units must be taken; the units may be distributed between one and two semesters in any way. To obtain credit a satisfactory thesis must be submitted at the end of the two semesters to the Electrical and Engineering and Computer Science Department archive. Students who complete four units and a thesis in one semester receive a letter grade at the end of H196A. Students who do not, receive an IP in H196A and must enroll in H196B.
Senior Honors Thesis Research: Read More [+]

Rules & Requirements

Prerequisites: Open only to students in the Electrical Engineering and Computer Science honors program

Hours & Format

Fall and/or spring: 15 weeks - 1-4 hours of independent study per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Letter grade. This is part one of a year long series course. A provisional grade of IP (in progress) will be applied and later replaced with the final grade after completing part two of the series. Final exam required.

Senior Honors Thesis Research: Read Less [-]

Terms offered: Spring 2016, Spring 2015, Spring 2014
Thesis work under the supervision of a faculty member. A minimum of four units must be taken; the units may be distributed between one and two semesters in any way. To obtain credit a satisfactory thesis must be submitted at the end of the two semesters to the Electrical and Engineering and Computer Science Department archive. Students who complete four units and a thesis in one semester receive a letter grade at the end of H196A. Students who do not, receive an IP in H196A and must enroll in H196B.
Senior Honors Thesis Research: Read More [+]

Rules & Requirements

Prerequisites: Open only to students in the Electrical Engineering and Computer Science honors program

Hours & Format

Fall and/or spring: 15 weeks - 1-4 hours of independent study per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Letter grade. This is part two of a year long series course. Upon completion, the final grade will be applied to both parts of the series. Final exam required.

Senior Honors Thesis Research: Read Less [-]

Terms offered: Spring 2018, Spring 2016, Fall 2015
Students take part in organized individual field sponsored programs with off-campus companies or tutoring/mentoring relevant to specific aspects and applications of computer science on or off campus. Note Summer CPT or OPT students: written report required. Course does not count toward major requirements, but will be counted in the cumulative units toward graduation.
Field Study: Read More [+]

Rules & Requirements

Prerequisites: Consent of instructor (see department adviser)

Repeat rules: Course may be repeated for credit without restriction.

Hours & Format

Fall and/or spring: 15 weeks - 1-4 hours of fieldwork per week

Summer:
6 weeks - 2.5-10 hours of fieldwork per week
8 weeks - 2-7.5 hours of fieldwork per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Offered for pass/not pass grade only. Final exam not required.

Field Study: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Spring 2019
Group study of selected topics in electrical engineering, usually relating to new developments.
Directed Group Study for Advanced Undergraduates: Read More [+]

Rules & Requirements

Prerequisites: 2.0 GPA or better; 60 units completed

Repeat rules: Course may be repeated for credit without restriction.

Hours & Format

Fall and/or spring: 15 weeks - 1-4 hours of directed group study per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Offered for pass/not pass grade only. Final exam not required.

Directed Group Study for Advanced Undergraduates: Read Less [-]

Terms offered: Fall 2018, Spring 2018, Fall 2017
Supervised independent study. Enrollment restrictions apply.
Supervised Independent Study: Read More [+]

Rules & Requirements

Prerequisites: Consent of instructor and major adviser

Credit Restrictions: Enrollment is restricted; see the Introduction to Courses and Curricula section of this catalog.

Repeat rules: Course may be repeated for credit without restriction.

Hours & Format

Fall and/or spring: 15 weeks - 0 hours of independent study per week

Summer:
6 weeks - 1-5 hours of independent study per week
8 weeks - 1-4 hours of independent study per week

Additional Details

Subject/Course Level: Electrical Engineering/Undergraduate

Grading/Final exam status: Offered for pass/not pass grade only. Final exam not required.

Supervised Independent Study: Read Less [-]

Faculty and Instructors

+ Indicates this faculty member is the recipient of the Distinguished Teaching Award.

Faculty

Pieter Abbeel, Associate Professor. Artificial Intelligence (AI), Control, Intelligent Systems, and Robotics (CIR), Machine Learning.
Research Profile

Elad Alon, Professor. Integrated Circuits (INC), Micro/Nano Electro Mechanical Systems (MEMS), Communications & Networking (COMNET), Design, Modeling and Analysis (DMA).
Research Profile

Venkat Anantharam, Professor. Communications & Networking (COMNET), Artificial Intelligence (AI), Control, Intelligent Systems, and Robotics (CIR), Security (SEC), Signal Processing (SP).
Research Profile

Murat Arcack, Professor. Control, Intelligent Systems, and Robotics (CIR), Biosystems & Computational Biology (BIO).
Research Profile

Ana Claudia Arias, Associate Professor. Physical Electronics (PHY), Flexible and Printed Electronics, Energy (ENE).

Krste Asanovic, Professor. Computer Architecture & Engineering (ARC), Integrated Circuits (INC), Operating Systems & Networking (OSNT),Design, Modeling and Analysis (DMA).
Research Profile

Babak Ayazifar, Professor. Education (EDUC), Signal processing and system theory EDUCATION: Development of pedagogical techniques and assessment tools , Signal Processing (SP), Graph signal processing.

Jonathan Bachrach, Adjunct Assistant Professor. Programming Systems (PS), Computer Architecture & Engineering (ARC), Design, Modeling and Analysis (DMA).

Ruzena Bajcsy, Professor. Artificial Intelligence (AI), Biosystems & Computational Biology (BIO), Control, Intelligent Systems, and Robotics (CIR), Graphics (GR), Human-Computer Interaction (HCI), Computer vision, Bridging information technology to humanities and social sciences, Security (SEC).
Research Profile

Brian A. Barsky, Professor. Computer science, geometric design and modeling, computer graphics, computer aided cornea modeling and visualization, medical imaging, virtual environments for surgical simulation.
Research Profile

Peter L. Bartlett, Professor. Statistics, machine learning, statistical learning theory, adaptive control.
Research Profile

Alexandre M. Bayen, Professor. Transportation, modelling and control of distributed parameters systems, large scale infrastructure systems, water distribution.
Research Profile

Jeffrey Bokor, Professor. Physical Electronics (PHY), Nanotechnology.
Research Profile

Bernhard Boser, Professor. Biosystems & Computational Biology (BIO), Design, Modeling and Analysis (DMA), Integrated Circuits (INC),Physical Electronics (PHY).
Research Profile

Eric Brewer, Professor. Operating Systems & Networking (OSNT), Energy (ENE), Security (SEC), Developing regions, Programming languages.
Research Profile

Duncan Callaway, Associate Professor.

John Canny, Professor. Computer science, activity-based computing, livenotes, mechatronic devices, flexonics.
Research Profile

Jose M. Carmena, Professor. Brain-machine interfaces, neural ensemble computation, neuroprosthetics, sensorimotor learning and control.
Research Profile

Constance Chang-Hasnain, Professor. Microsystems and materials, Nano-Optoelectronic devices.

Alessandro Chiesa, Assistant Professor. Security (SEC), Theory (THY).

John Chuang, Professor. Computer networking, computer security, economic incentives, ICTD.
Research Profile

Phillip Colella, Professor in Residence.

Steven Conolly, Professor. Medical imaging instrumentation and control.
Research Profile

Thomas Courtade, Assistant Professor. Communications & Networking (COMNET).
Research Profile

David E. Culler, Professor. Computer Architecture & Engineering (ARC), Energy (ENE), Operating Systems & Networking (OSNT),Programming Systems (PS), Security (SEC), Parallel architecture, High-performance networks, Workstation clusters.
Research Profile

Trevor Darrell, Professor in Residence. Artificial Intelligence (AI), Control, Intelligent Systems, and Robotics (CIR), Computer Vision.

James W. Demmel, Professor. Computer science, scientific computing, numerical analysis, linear algebra.
Research Profile

+ John DeNero, Assistant Teaching Professor. Artificial Intelligence (AI), Education (EDUC).

Anca Dragan, Assistant Professor. Artificial Intelligence (AI), Control, Intelligent Systems, and Robotics (CIR), Human-Computer Interaction (HCI).

Prabal Dutta, Associate Professor.

Alexei (Alyosha) Efros, Associate Professor. Computer Vision, Graphics (GR), Artificial Intelligence (AI).
Research Profile

Laurent El Ghaoui, Professor. Decision-making under uncertainty, convex optimization, robust solutions, semidefinite programming, exhaustive simulation.
Research Profile

Ronald S. Fearing, Professor. Control, Intelligent Systems, and Robotics (CIR), Biosystems & Computational Biology (BIO).

Armando Fox, Professor. Programming systems (PS), Education (EDUC), Operating Systems and Networking (OSNT).
Research Profile

Michael Franklin, Adjunct Professor. Operating Systems & Networking (OSNT), AMPLab.

Gerald Friedland, Adjunct Assistant Professor.

+ Robert J. Full, Professor. Energetics, comparative biomechanics, arthropod, adhesion, comparative physiology, locomotion, neuromechanics, biomimicry, biological inspiration, reptile, gecko, amphibian, robots, artificial muscles.
Research Profile

Jack L. Gallant, Professor. Vision science, form vision, attention, fMRI, computational neuroscience, natural scene perception, brain encoding, brain decoding.
Research Profile

Dan Garcia, Teaching Professor. Education (EDUC), Computational Game Theory, Graphics (GR).

Sanjam Garg, Assistant Professor. Theory (THY), Security (SEC).
Research Profile

Ali Ghodsi, Adjunct Assistant Professor. Database Management Systems (DBMS), Operating Systems & Networking (OSNT).

Ken Goldberg, Professor. Robotics, art, social media, new media, automation.
Research Profile

Joseph Gonzalez, Assistant Professor. Artificial Intelligence (AI), Database Management Systems (DBMS).

Moritz Hardt, Assistant Profesor.

Bjoern Hartmann, Associate Professor. Human-Computer Interaction (HCI), Graphics (GR), Programming Systems (PS).

Marti A. Hearst, Professor. Information retrieval, human-computer interaction, user interfaces, information visualization, web search, search user interfaces, empirical computational linguistics, natural language processing, text mining, social media.
Research Profile

Joseph M. Hellerstein, Professor. Database Management Systems (DBMS), Operating Systems & Networking (OSNT).
Research Profile

Paul N. Hilfinger, Teaching Professor. Programming Systems (PS), Scientific Computing (SCI), Software engineering, Parallel programming techniques.
Research Profile

Joshua Hug, Assistant Teaching Professor. Education (EDUC), Computer Science education.

Ali Javey, Professor. Physical Electronics (PHY), Energy (ENE), Micro/Nano Electro Mechanical Systems (MEMS), Nanomaterials and Nanotechnology.
Research Profile

Michael I. Jordan, Professor. Computer science, artificial intelligence, bioinformatics, statistics, machine learning, electrical engineering, applied statistics, optimization.
Research Profile

Anthony D. Joseph, Professor. Operating Systems & Networking (OSNT), Security (SEC), Computer and Network Security, Distributed systems, Mobile computing, Wireless networking, Software engineering, and operating systems.
Research Profile

+ Richard Karp, Professor. Computational molecular biology, genomics, DNA molecules, structure of genetic regulatory networks, combinatorial and statsitical methods.
Research Profile

Randy H. Katz, Professor. Computer Architecture & Engineering (ARC), Operating Systems & Networking (OSNT), Distributed and networked systems design and implementation.
Research Profile

Kurt Keutzer, Professor. Computer Architecture & Engineering (ARC), Design, Modeling and Analysis (DMA), Scientific Computing (SCI).
Research Profile

Daniel Klein, Professor. Artificial Intelligence (AI), Natural Language Processing, Computational Linguistics, Machine Learning.
Research Profile

John D. Kubiatowicz, Professor. Operating Systems & Networking (OSNT), Security (SEC), Computer architecture, Quantum computer design, Internet-scale storage systems, Peer-to-peer networking.
Research Profile

Andreas Kuehlmann, Adjunct Professor. Design, Modeling and Analysis (DMA).
Research Profile

Edward A. Lee, Professor. Embedded Software, Real-Time Systems, Cyber-Physical Systems, Concurrency, Design, Modeling and Analysis (DMA), Programming Systems (PS),Signal Processing (SP).
Research Profile

Luke Lee, Professor. Biophotonics, biophysics, bionanoscience, molecular imaging, single cell analysis, bio-nano interfaces, integrated microfluidic devices (iMD) for diagnostics and preventive personalized medicine.
Research Profile

Sergey Levine, Assisstant Professor.

Chunlei Liu, Associate Professor.

Tsu-Jae King Liu, Professor. Physical Electronics (PHY), Micro/Nano Electro Mechanical Systems (MEMS).
Research Profile

Michael Lustig, Associate Professor. Medical Imaging, Magnetic Resonance Imaging, Signal Processing (SP), Scientific Computing (SCI), Physical Electronics (PHY), Communications & Networking (COMNET), Biosystems & Computational Biology (BIO), Control, Intelligent Systems, and Robotics (CIR).

Michel Maharbiz, Professor. Neural interfaces, bioMEMS, microsystems, MEMS, microsystems for the life sciences.
Research Profile

Jitendra Malik, Professor. Artificial Intelligence (AI), Biosystems & Computational Biology (BIO), Control, Intelligent Systems, and Robotics (CIR), Graphics (GR), Human-Computer Interaction (HCI), Signal Processing (SP),.
Research Profile

Elchanan Mossel, Professor. Applied probability, statistics, mathematics, finite markov chains, markov random fields, phlylogeny.
Research Profile

Rikky Muller, Assistant Professor. Integrated Circuits (INC), Biosystems & Computational Biology (BIO), Micro/Nano Electro Mechanical Systems (MEMS).

George Necula, Professor. Software engineering, programming systemsm, security, program analysis.
Research Profile

Ren Ng, Assistant Professor. Imaging Systems, Computational Photography,, Signal Processing (SP), Optics.

Clark Nguyen, Professor. Micro/Nano Electro Mechanical Systems (MEMS), Integrated Circuits (INC), Physical Electronics (PHY), Design, Modeling and Analysis (DMA).
Research Profile

Ali Niknejad, Professor. Integrated Circuits (INC), Microwave and mm-Wave Circuits and Systems, Physical Electronics (PHY), Signal Processing (SP), Applied Electromagnetics, Communications & Networking (COMNET), Design, Modeling and Analysis (DMA).
Research Profile

Borivoje Nikolic, Professor. Integrated Circuits (INC), Communications & Networking (COMNET), Design, Modeling and Analysis (DMA), Computer Architecture & Engineering (ARC).
Research Profile

James O'Brien, Professor. Computer graphics, fluid dynamics, computer simulation, physically based animation, finite element simulation, human perception, image forensics, video forensics, computer animation, special effects for film, video game technology, motion capture .

Bruno Olshausen, Professor. Visual perception, computational neuroscience, computational vision.
Research Profile

Lior Pachter, Professor. Mathematics, applications of statistics, combinatorics to problems in biology.
Research Profile

Abhay Parekh, Adjunct Professor. Communications & Networking (COMNET).

Shyam Parekh, Adjunct Associate Professor. Communications & Networking (COMNET).

Eric Paulos, Associate Professor. Human-Computer Interaction (HCI), New Media arts.

Vern Paxson, Professor. Security (SEC), Operating Systems & Networking (OSNT).
Research Profile

Kristofer Pister, Professor. Micro/Nano Electro Mechanical Systems (MEMS), Control, Intelligent Systems, and Robotics (CIR), Micro-robotics, Integrated Circuits (INC), Low-power circuits.
Research Profile

+ Kameshwar Poolla, Professor. Cybersecurity, modeling, control, renewable energy, estimation, integrated circuit design and manufacturing, smart grids.
Research Profile

Raluca Ada Popa, Assistant Professor. Operating Systems & Networking (OSNT), Security (SEC).

Jan M. Rabaey, Professor. Communications & Networking (COMNET), Design, Modeling and Analysis (DMA), Energy (ENE), Integrated Circuits (INC), Signal Processing (SP), Computer architecture.
Research Profile

Jonathan Ragan-Kelley, Assistant Professor.

Prasad Raghavendra, Associate Professor. Theory (THY).

Ravi Ramamoorthi, Professor. Graphics (GR), Scientific Computing (SCI), Signal Processing (SP), Computer Vision.

Kannan Ramchandran, Professor. Communications & Networking (COMNET), Signal Processing (SP), Control, Intelligent Systems, and Robotics (CIR).
Research Profile

Gireeja Ranade, Assistant Professor.

Satish Rao, Professor. Biosystems & Computational Biology (BIO), Theory (THY).
Research Profile

Sylvia Ratnasamy, Associate Professor. Operating Systems & Networking (OSNT).

Benjamin Recht, Associate Professor. Control, Intelligent Systems, and Robotics (CIR), Signal Processing (SP), Machine Learning (ML), Optimization (OPT).

Jaijeet Roychowdhury, Professor. Design, Modeling and Analysis (DMA), Scientific Computing (SCI), Biosystems & Computational Biology (BIO).

Stuart Russell, Professor. Artificial intelligence, computational biology, algorithms, machine learning, real-time decision-making, probabilistic reasoning.
Research Profile

Anant Sahai, Associate Professor. Communications & Networking (COMNET), Information Theory, Cognitive Radio and Spectrum Sharing, Control, Intelligent Systems, and Robotics (CIR), Distributed and Networked Control, Signal Processing (SP), Theory (THY), Information Theory.
Research Profile

Sayeef Salahuddin, Associate Professor. Physical Electronics (PHY), Design, Modeling and Analysis (DMA), Energy (ENE), Scientific Computing (SCI).

Seth R. Sanders, Professor. Energy (ENE), Control, Intelligent Systems, and Robotics (CIR), Integrated Circuits (INC), Power and electronics systems.
Research Profile

Alberto L. Sangiovanni-Vincentelli, Professor. Design, Modeling and Analysis (DMA), Embedded System Design, Design methodologies and tools, Control, Intelligent Systems, and Robotics (CIR), Hybrid systems, Design methodologies and tools, Communications & Networking (COMNET), Wireless sensor network design, Design methodologies and tools.
Research Profile

S. Shankar Sastry, Professor. Computer science, robotics, arial robots, cybersecurity, cyber defense, homeland defense, nonholonomic systems, control of hybrid systems, sensor networks, interactive visualization, robotic telesurgery, rapid prototyping.
Research Profile

Koushik Sen, Associate Professor. Programming Systems (PS), Software Engineering, Programming Languages, and Formal Methods: Software Testing, Verification, Model Checking, Runtime Monitoring, Performance Evaluation, and Computational Logic , Security (SEC).
Research Profile

Sanjit Seshia, Professor. Electronic design automation, theory, computer security, program analysis, dependable computing, computational logic, formal methods.
Research Profile

Scott Shenker, Professor. Internet Architecture, Software-Defined Networks, Datacenter Infrastructure, Large-Scale Distributed Systems, Game Theory and Economics,Operating Systems & Networking (OSNT).
Research Profile

Jonathan Shewchuk, Professor. Scientific Computing (SCI), Theory (THY), Graphics (GR).
Research Profile

Alistair Sinclair, Professor. Theory (THY), Randomized algorithms, applied probability, statistical physics.
Research Profile

Dawn Song, Professor. Operating Systems & Networking (OSNT), Security (SEC), Programming Systems (PS).
Research Profile

Yun Song, Professor. Computational biology, population genomics, applied probability and statistics.
Research Profile

Costas J. Spanos, Professor. Energy (ENE), Integrated Circuits (INC), Physical Electronics (PHY), Semiconductor manufacturing, Solid-State Devices.
Research Profile

Ian Stoica, Professor. Operating Systems & Networking (OSNT), Security (SEC), Networking and distributed computer systems, Quality of Service (Q of S) and resources management, modeling and performance analysis.

Vladimir Stojanovic, Associate Professor. Integrated Circuits (INC), Micro/Nano Electro Mechanical Systems (MEMS), Computer Architecture & Engineering (ARC), Physical Electronics (PHY), Communications & Networking (COMNET), Integrated Photonics, Circuit design with Emerging-Technologies.
Research Profile

Bernd Sturmfels, Professor. Mathematics, combinatorics, computational algebraic geometry.
Research Profile

Vivek Subramanian, Professor. Physical Electronics (PHY), Energy (ENE), Integrated Circuits (INC).
Research Profile

Claire Tomlin, Professor. Control, Intelligent Systems, and Robotics (CIR), Biosystems & Computational Biology (BIO), Control theory, hybrid and embedded systems, biological cell networks.
Research Profile

Luca Trevisan, Professor. Theory (THY), (Computational Complexity, Randomness in Computation, Combinatorial Optimization), Security (SEC).

Stavros Tripakis, Adjunct Associate Professor. Design, Modeling and Analysis (DMA), Computer-Aided System Design, Formal Methods, Verification, Synthesis, Embedded and Cyber-Physical Systems, Programming Systems (PS).

David Tse, Adjunct Professor. Communications & Networking (COMNET).
Research Profile

Doug Tygar, Professor. Privacy, technology policy, computer security, electronic commerce, software engineering, reliable systems, embedded systems, computer networks, cryptography, cryptology, authentication, ad hoc networks.
Research Profile

Umesh Vazirani, Professor. Quantum computation, hamiltonian complexity, analysis of algorithms.
Research Profile

Alexandra von Meier, Adjunct Professor. Energy (ENE), Electric Grids, Power Distribution.

David Wagner, Professor. Security (SEC).
Research Profile

Martin Wainwright, Professor. Statistical machine learning, High-dimensional statistics, information theory, Optimization and algorithmss .
Research Profile

Laura Waller, Associate Professor. Physical Electronics (PHY), Signal Processing (SP), Computational imaging, Optics and Imaging, Biosystems & Computational Biology (BIO), Graphics (GR).
Research Profile

Jean Walrand, Professor. Communications & Networking (COMNET), Performance evaluation, Game theory.
Research Profile

John Wawrzynek, Professor. Computer Architecture & Engineering (ARC).
Research Profile

Adam Wolisz, Adjunct Professor. Communications & Networking (COMNET), Computer Architecture & Engineering (ARC), System Performance Evaluation.

Ming C. Wu, Professor. Si photonics, optoelectronics, nanophotonics, optical MEMS, Optofluidics, Micro/Nano Electro Mechanical Systems (MEMS), Physical Electronics (PHY).

Eli Yablonovitch, Professor. Optoelectronics Research Group, high speed optical communications, photonic crystals at optical and microwave frequencies, the milli-Volt switch, optical antennas and solar cells , Physical Electronics (PHY).
Research Profile

Katherine A. Yelick, Professor. Programming Systems (PS), Scientific Computing (SCI), Biosystems & Computational Biology (BIO), parallel programming techniques.
Research Profile

Nir Yosef, Assistant Professor. Computational biology.
Research Profile

Bin Yu, Professor. Neuroscience, remote sensing, networks, statistical machine learning, high-dimensional inference, massive data problems, document summarization .
Research Profile

Avideh Zakhor, Professor. Signal Processing (SP), Artificial Intelligence (AI), Control, Intelligent Systems, and Robotics (CIR), Graphics (GR).
Research Profile

Emeritus Faculty

David Attwood, Professor Emeritus. Short wavelength electromagnetics, Soft X-ray microscopy, Coherence, EUV lithography.
Research Profile

Elwyn R. Berlekamp, Professor Emeritus. Computer science, electrical engineering, mathematics, combinatorial game theory, algebraic coding theory.
Research Profile

Manuel Blum, Professor Emeritus. Recursive function, cryptographic protocols, program checking.

Robert K. Brayton, Professor Emeritus. Design, Modeling and Analysis (DMA), Advanced methods in combinational and sequential logic synthesis and formal verification.
Research Profile

Robert W. Brodersen, Professor Emeritus. Design, Modeling and Analysis (DMA), Integrated Circuits (INC), Signal Processing (SP).

Thomas F. Budinger, Professor Emeritus. Image processing, biomedical electronics, quantitative aging, cardiovascular physiology, bioastronautics, image reconstruction, nuclear magnetic resonance, positron emission, tomography, reconstruction tomography, inverse problem mathematics.
Research Profile

Leon O. Chua, Professor Emeritus. Biosystems & Computational Biology (BIO), Control, Intelligent Systems, and Robotics (CIR), Cellular neural networks, Cellular automata, Complexity,, Nanoelectronics, Nonlinear circuits and systems, Nonlinear dynamics, Chaos,.
Research Profile

Mike Clancy, Professor Emeritus. Science education, cognitive development, educational software.
Research Profile

Richard J. Fateman, Professor Emeritus. Artificial Intelligence (AI), Scientific Computing (SCI), Computer algebra systems, Programming environments and systems, Programming languages and compilers, Symbolic mathematical computation, Document image analysis, multimodal input of mathematics.
Research Profile

Jerome A. Feldman, Professor Emeritus. Artificial Intelligence (AI), Biosystems & Computational Biology (BIO), Security (SEC), cognitive science.
Research Profile

Domenico Ferrari, Professor Emeritus. UC Berkeley Unix Project, high-speed network testbeds and the design of real-time communication services and network protocols for multimedia traffic.

Susan L. Graham, Professor Emeritus. Graphics (GR), Human-Computer Interaction (HCI), Programming Systems (PS), Scientific Computing (SCI), Software development environments, software engineering.
Research Profile

Paul R. Gray, Professor Emeritus. Design, Modeling and Analysis (DMA), Integrated Circuits (INC).
Research Profile

T. Kenneth Gustafson, Professor Emeritus. Solid-State Devices, Basic electromagnetic and quantum applications.

Michael A. Harrison, Professor Emeritus. Multimedia, User interfaces, Software environments.

Brian K. Harvey, Professor Emeritus. Education (EDUC).
Research Profile

David A. Hodges, Professor Emeritus. Integrated Circuits (INC).

Chenming Hu, Professor Emeritus. Semiconductor Device Technologies.
Research Profile

William M. Kahan, Professor Emeritus. Computer Architecture & Engineering (ARC), Scientific Computing (SCI), Computer architecture, Scientific computing, Numerical analysis.
Research Profile

Edward L. Keller, Professor Emeritus. Computational neuroscience, bioengineering, neurophysiology of the oculomotor system.

Kam Y. Lau, Professor Emeritus. Communications & Networking (COMNET), Optoelectronic devices, Microwave and millimeter wave signal transport over optical fiber links.
Research Profile

Edwin R. Lewis, Professor Emeritus.
Research Profile

Allan J. Lichtenberg, Professor Emeritus. Nano-Optoelectronics, Electromagnetics/Plasmas, Energy (ENE).
Research Profile

Michael A. Lieberman, Professor Emeritus. Plasma-assisted materials processing, Energy (ENE).
Research Profile

Kenneth K. Mei, Professor Emeritus. Nano-Optoelectronics, Electromagnetics/Plasmas.

David G. Messerschmitt, Professor Emeritus. Communications & Networking (COMNET), Signal Processing (SP), Business and economics issues in the software industry.

Robert G. Meyer, Professor Emeritus. Integrated Circuits (INC).
Research Profile

Nelson Morgan, Professor Emeritus. Signal Processing (SP).

+ Richard Muller, Professor Emeritus. Astrophysics, geophysics, physics, elementary particle physics, cosmic micro wave background, supernovae for cosmology, origin of the earth's magnetic flips, Nemesis theory, glacial cycles, red sprites, lunar impacts, iridium measurement.
Research Profile

Andrew R. Neureuther, Professor Emeritus. Integrated Circuits (INC), Solid-State Devices.
Research Profile

William G. Oldham, Professor Emeritus. Integrated circuits, Semiconductor manufacturing.
Research Profile

Beresford N. Parlett, Professor Emeritus.

David A. Patterson, Professor Emeritus. Professor in the Graduate School: Computer Architecture & Engineering (ARC), Computer Architecture and Systems: Parallel Computing performance, correctness, productivity,Biosystems & Computational Biology (BIO), Cancer tumor genomics, Operating Systems & Networking (OSNT).
Research Profile

Elijah Polak, Professor Emeritus. Control, Intelligent Systems, and Robotics (CIR), Numerical methods for engineering optimization.
Research Profile

Chittoor V. Ramamoorthy, Professor Emeritus. Software engineering.

Lawrence A. Rowe, Professor Emeritus. Multimedia Technology.
Research Profile

Steven E. Schwarz, Professor Emeritus. Solid-State Devices, Nano-Optoelectronics, Electromagnetics/Plasmas.

Carlo H. Sequin, Professor Emeritus. Geometric modeling, Artistic geometry, Mathematical visualizations , Graphics (GR), Human-Computer Interaction (HCI), CAD tools.

Jerome R. Singer, Professor Emeritus.

Alan J. Smith, Professor Emeritus. Computer Architecture & Engineering (ARC), Operating Systems & Networking (OSNT), Computer System Performance Analysis, I/O Systems, Cache Memories, Memory Systems.

Michael Stonebraker, Professor Emeritus. Database Technology.

Aram J. Thomasian, Professor Emeritus.
Research Profile

Theodore Van Duzer, Professor Emeritus. Superconductor Electronics.
Research Profile

Pravin Varaiya, Professor Emeritus. Communications & Networking (COMNET), Control, Intelligent Systems, and Robotics (CIR), Energy (ENE), Control, Networks, Power systems, Transportation.

William J. (Jack) Welch, Professor Emeritus. Nano-Optoelectronics, Electromagnetics/Plasmas.
Research Profile

Richard M. White, Professor Emeritus. Energy (ENE), Solid-State Devices.

Eugene Wong, Professor Emeritus. Communications & Networking (COMNET).
Research Profile

Felix F. Wu, Professor Emeritus. Electric power systems analysis, generation and transmission systems planning and investment, power system control and communications, electric energy industry restructuring .
Research Profile

Lotfi A. Zadeh, Professor Emeritus. Artificial intelligence, linguistics, control theory, logic, fuzzy sets, decision analysis, expert systems neural networks, soft computing, computing with words, computational theory of perceptions and precisiated natural language.
Research Profile

Contact Information

Department of Electrical Engineering and Computer Sciences

253 Cory Hall

Phone: 510-642-3214

Fax: 510-643-7846

Visit Department Website

Department Chair

Jeff Bokor, PhD

231 Cory Hall

Phone: 510-642-4134

jbokor@eecs.berkeley.edu

Associate Chair

John Canny, PhD

637 Soda Hall, MC 1770

Phone: 510-642-9955

jfc@cs.berkeley.edu

Vice-Chair, Undergraduate Matters

John Denero, PhD

781 Soda Hall

Phone: 510-643-7354

denero@eecs.berkeley.edu

Vice-Chair, Master’s Degree Programs

Vladimir Stojanovic, PhD

513 Cory Hall

Phone: (510) 664-4322

vlada@berkeley.edu

EE Division Vice Chair, Graduate Matters

Ronald S. Fearing, PhD

725 Sutardja Dai Hall

Phone: 510-642-9193

CS Division Vice Chair, Graduate Matters

John Wawrzynek, PhD

631 Soda Hall

Phone: 510-643-9434

johnw@cs.berkeley.edu

Executive Director, Center for Student Affairs

Susanne Kauer

221 Cory Hall

Phone: 510-642-3694

skauer@eecs.berkeley.edu

Electrical Engineering and Computer Sciences

Overview

Undergraduate Programs

Graduate Programs

Courses

Select a subject to view courses

Electrical Engineering and Computer Sciences

EECS 16A Designing Information Devices and Systems I 4 Units

EECS 16B Designing Information Devices and Systems II 4 Units

EECS 47D Completion of work in Electrical Engineering 16A 1 - 3 Units

EECS 47E Completion of work in Electrical Engineering 16B 1 - 3 Units

EECS 47F Completion of work in Computer Science 70 1 - 3 Units

EECS C106A Introduction to Robotics 4 Units

EECS C106B Robotic Manipulation and Interaction 4 Units

EECS 126 Probability and Random Processes 4 Units

EECS 127 Optimization Models in Engineering 4 Units

EECS 149 Introduction to Embedded Systems 4 Units

EECS 151 Introduction to Digital Design and Integrated Circuits 3 Units

EECS 151LA Application Specific Integrated Circuits Laboratory 2 Units

EECS 151LB Field-Programmable Gate Array Laboratory 2 Units

EECS 206A Introduction to Robotics 4 Units

EECS 206B Robotic Manipulation and Interaction 4 Units

EECS 219C Formal Methods: Specification, Verification, and Synthesis 3 Units

EECS 225A Statistical Signal Processing 3 Units

EECS 225B Digital Image Processing 3 Units

EECS 227AT Optimization Models in Engineering 4 Units

EECS 251A Introduction to Digital Design and Integrated Circuits 3 Units

EECS 251LA Introduction to Digital Design and Integrated Circuits Lab 2 Units

EECS 251LB Introduction to Digital Design and Integrated Circuits Lab 2 Units

Computer Science

COMPSCI C6 Introduction to Computational Thinking with Data 3 Units

COMPSCI C8 Foundations of Data Science 4 Units

COMPSCI 9A Matlab for Programmers 2 Units

COMPSCI 9C C for Programmers 2 Units

COMPSCI 9D Scheme and Functional Programming for Programmers 2 Units

COMPSCI 9E Productive Use of the UNIX Environment 2 Units

COMPSCI 9F C++ for Programmers 2 Units

COMPSCI 9G JAVA for Programmers 2 Units

COMPSCI 9H Python for Programmers 2 Units

COMPSCI 10 The Beauty and Joy of Computing 4 Units

COMPSCI W10 The Beauty and Joy of Computing 4 Units

COMPSCI 36 CS Scholars Seminar: The Educational Climate in CS & CS61A technical discussions 2 Units

COMPSCI 39 Freshman/Sophomore Seminar 1.5 - 2 Units

COMPSCI 39J Freshman/Sophomore Seminar 1.5 - 4 Units

COMPSCI 39K Freshman/Sophomore Seminar 1.5 - 4 Units

COMPSCI 39M Freshman/Sophomore Seminar 1.5 - 4 Units

COMPSCI 39N Freshman/Sophomore Seminar 1.5 - 4 Units

COMPSCI 39P Freshman/Sophomore Seminar 1.5 - 4 Units

COMPSCI 39Q Freshman/Sophomore Seminar 1.5 - 4 Units

COMPSCI 39R Freshman/Sophomore Seminar 1.5 - 4 Units

COMPSCI 47A Completion of Work in Computer Science 61A 1 Unit

COMPSCI 47B Completion of Work in Computer Science 61B 1 Unit

COMPSCI 47C Completion of Work in Computer Science 61C 1 Unit

COMPSCI 61A The Structure and Interpretation of Computer Programs 4 Units

COMPSCI 61B Data Structures 4 Units

COMPSCI 61BL Data Structures and Programming Methodology 4 Units

COMPSCI 61C Great Ideas of Computer Architecture (Machine Structures) 4 Units

COMPSCI 61CL Machine Structures (Lab-Centric) 4 Units

COMPSCI W61A The Structure and Interpretation of Computer Programs (Online) 4 Units

COMPSCI W61B Data Structures (Online) 4 Units

COMPSCI 70 Discrete Mathematics and Probability Theory 4 Units

COMPSCI C79 Societal Risks and the Law 3 Units

COMPSCI 88 Computational Structures in Data Science 3 Units

COMPSCI 94 Special Topics 1 - 4 Units

COMPSCI 97 Field Study 1 - 4 Units

COMPSCI 98 Directed Group Study 1 - 4 Units

COMPSCI 99 Individual Study and Research for Undergraduates 1 - 2 Units

COMPSCI C100 Principles & Techniques of Data Science 4 Units

COMPSCI 146L Programmable Digital Systems Laboratory 2 Units

COMPSCI 152 Computer Architecture and Engineering 4 Units

COMPSCI 160 User Interface Design and Development 4 Units

COMPSCI 161 Computer Security 4 Units

COMPSCI 162 Operating Systems and System Programming 4 Units

COMPSCI 164 Programming Languages and Compilers 4 Units

COMPSCI 168 Introduction to the Internet: Architecture and Protocols 4 Units

COMPSCI 169 Software Engineering 4 Units

COMPSCI 169A Introduction to Software Engineering 3 Units

COMPSCI 169L Software Engineering Team Project 2 Units

COMPSCI W169A Software Engineering 3 Units

COMPSCI 170 Efficient Algorithms and Intractable Problems 4 Units