Computer Science < 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

About the Program

The Department of Electrical Engineering and Computer Sciences (EECS) offers two graduate programs in Computer Science: the Master of Science (MS), and the Doctor of Philosophy (PhD).

Master of Science (MS)

The Master of Science (MS) emphasizes research preparation and experience and, for most students, is a chance to lay the groundwork for pursuing a PhD.

Doctor of Philosophy (PhD)

The Berkeley PhD in EECS combines coursework and original research with some of the finest EECS faculty in the US preparing for careers in academia or industry. Our alumni have gone on to hold amazing positions around the world.

Visit Department Website

Admissions

Admission to the University

Minimum Requirements for Admission

The following minimum requirements apply to all graduate programs and will be verified by the Graduate Division:

A bachelor’s degree or recognized equivalent from an accredited institution;
A grade point average of B or better (3.0);
If the applicant comes from a country or political entity (e.g., Quebec) where English is not the official language, adequate proficiency in English to do graduate work, as evidenced by a TOEFL score of at least 90 on the iBT test, 570 on the paper-and-pencil test, or an IELTS Band score of at least 7 on a 9-point scale (note that individual programs may set higher levels for any of these); and
Sufficient undergraduate training to do graduate work in the given field.

Applicants Who Already Hold a Graduate Degree

The Graduate Council views academic degrees not as vocational training certificates, but as evidence of broad training in research methods, independent study, and articulation of learning. Therefore, applicants who already have academic graduate degrees should be able to pursue new subject matter at an advanced level without the need to enroll in a related or similar graduate program.

Programs may consider students for an additional academic master’s or professional master’s degree only if the additional degree is in a distinctly different field.

Applicants admitted to a doctoral program that requires a master’s degree to be earned at Berkeley as a prerequisite (even though the applicant already has a master’s degree from another institution in the same or a closely allied field of study) will be permitted to undertake the second master’s degree, despite the overlap in field.

The Graduate Division will admit students for a second doctoral degree only if they meet the following guidelines:

Applicants with doctoral degrees may be admitted for an additional doctoral degree only if that degree program is in a general area of knowledge distinctly different from the field in which they earned their original degree. For example, a physics PhD could be admitted to a doctoral degree program in music or history; however, a student with a doctoral degree in mathematics would not be permitted to add a PhD in statistics.
Applicants who hold the PhD degree may be admitted to a professional doctorate or professional master’s degree program if there is no duplication of training involved.

Applicants may apply only to one single degree program or one concurrent degree program per admission cycle.

Required Documents for Applications

Transcripts: Applicants may upload unofficial transcripts with your application for the departmental initial review. If the applicant is admitted, then official transcripts of all college-level work will be required. Official transcripts must be in sealed envelopes as issued by the school(s) attended. If you have attended Berkeley, upload your unofficial transcript with your application for the departmental initial review. If you are admitted, an official transcript with evidence of degree conferral will not be required.
Letters of recommendation: Applicants may request online letters of recommendation through the online application system. Hard copies of recommendation letters must be sent directly to the program, not the Graduate Division.
Evidence of English language proficiency: All applicants from countries or political entities in which the official language is not English are required to submit official evidence of English language proficiency. This applies to applicants from Bangladesh, Burma, Nepal, India, Pakistan, Latin America, the Middle East, the People’s Republic of China, Taiwan, Japan, Korea, Southeast Asia, most European countries, and Quebec (Canada). However, applicants who, at the time of application, have already completed at least one year of full-time academic course work with grades of B or better at a US university may submit an official transcript from the US university to fulfill this requirement. The following courses will not fulfill this requirement:
- courses in English as a Second Language,
- courses conducted in a language other than English,
- courses that will be completed after the application is submitted, and
- courses of a non-academic nature.

If applicants have previously been denied admission to Berkeley on the basis of their English language proficiency, they must submit new test scores that meet the current minimum from one of the standardized tests. Official TOEFL score reports must be sent directly from Educational Test Services (ETS). The institution code for Berkeley is 4833. Official IELTS score reports must be mailed directly to our office from the British Council. TOEFL and IELTS score reports are only valid for two years.

Where to Apply

Visit the Berkeley Graduate Division application page.

Admission to the Program

The following items are required for admission to the Berkeley EECS MS/PhD program in addition to the University’s general graduate admissions requirements:

GRE Scores: All three sections of the GRE are required. Send your scores electronically to Institution Code 4833. (Scores must be from the last five years.)
Statement of Purpose: Why are you applying for this program? What will you do during this degree program? What do you want to do after and how will this help you?
Personal History Statement: What from your past made you decide to go into this field? And how will your personal history help you succeed in this program and your future goals?
GPA: If you attended a university outside of the USA, please leave the GPA section blank.
Resume: Please also include a full resume/CV listing your experience and education.

Complete the online UC Berkeley graduate application:

Start your application through this link, and fill in each relevant page.
Upload the materials above, and send the recommender links several weeks prior to the application deadline, to give your recommenders time to submit their letters.

Doctoral Degree Requirements

Normative Time Requirements

Normative time in the EECS department is between 5.5-6 years for the doctoral program.

Time to Advancement

Curriculum

The faculty of the College of Engineering recommends a minimum number of courses taken while in graduate standing. The total minimum is 24 units of coursework, taken for a letter grade and not including 397, 298, 299, 301, 375 and 602.

Course List
Code	Title	Units
12 units from one major field within EECS, with a 3.5 grade point average		12
6 units from one minor field within EECS, with a 3.0 grade point average		6
6 units from one minor field outside EECS, with a 3.0 grade point average		6

Preliminary Exams

The EECS preliminary requirement consists of two components.

Oral Examination

The oral exam serves an advisory role in a student's graduate studies program, giving official feedback from the exam committee of faculty members. Students must be able to demonstrate an integrated grasp of the exam area's body of knowledge in an unstructured framework. Students must pass the oral portion of the preliminary exam within their first two attempts. A third attempt is possible with a petition of support from the student's faculty adviser and final approval by the prelim committee chair. Failure to pass the oral portion of the preliminary exam will result in the student being ineligible to complete the PhD program. The examining committee awards a score in the range of 0-10. The minimum passing score is 6.0.

Breadth Courses

The breadth courses ensure that students have exposure to areas outside of their concentration. It is expected that students achieve high academic standards in these courses.

CS students must complete courses from three of the following areas, passing each with at least a B+. One course must be selected from the Theory, AI, or Graphics/HCI group; and one course must be selected from the Programming, Systems, or Architecture/VLSI group¹.

Course List
Code	Title	Units
Theory
COMPSCI 270	Combinatorial Algorithms and Data Structures	3
COMPSCI 271	Randomness and Computation	3
COMPSCI 273	Foundations of Parallel Computation	3
COMPSCI 274	Computational Geometry	3
COMPSCI 276	Cryptography	3
AI
COMPSCI C280	Computer Vision	3
COMPSCI C281A	Statistical Learning Theory	3
COMPSCI C281B	Advanced Topics in Learning and Decision Making	3
COMPSCI 287	Advanced Robotics	3
COMPSCI 288	Natural Language Processing	4
COMPSCI 289A	Introduction to Machine Learning	4
Graphics/HCI
COMPSCI 260B	Human-Computer Interaction Research	3
Programming
COMPSCI 263	Design of Programming Languages	3
COMPSCI 264	Implementation of Programming Languages	4
COMPSCI 265	Compiler Optimization and Code Generation	3
COMPSCI C267	Applications of Parallel Computers	3
EECS 219C	Formal Methods: Specification, Verification, and Synthesis	3
Systems
COMPSCI 261	Security in Computer Systems	3
COMPSCI 261N	Internet and Network Security	4
COMPSCI 262A	Advanced Topics in Computer Systems	4
COMPSCI 262B	Advanced Topics in Computer Systems	3
COMPSCI 268	Computer Networks	3
COMPSCI 286B	Implementation of Data Base Systems	3
Architecture/VLSI
COMPSCI 250	VLSI Systems Design	4
COMPSCI 252	Graduate Computer Architecture	4

COMPSCI 260B, COMPSCI 263, and EL ENG 219C cannot be used to fulfill this constraint, though they can be used to complete one of the three courses.

Qualifying Examination (QE)

The QE is an important checkpoint meant to show that a student is on a promising research track toward the PhD degree. It is a University examination, administered by the Graduate Council, with the specific purpose of demonstrating that "the student is clearly an expert in those areas of the discipline that have been specified for the examination, and that he or she can, in all likelihood, design and produce an acceptable dissertation." Despite such rigid criteria, faculty examiners recognize that the level of expertise expected is that appropriate for a third year graduate student who may be only in the early stages of a research project.

The EECS Department offers the qualifying exam in two formats: A or B. Students may choose the exam type of their choice after consultation with their adviser.

Format A

Students prepare a write-up and presentation summarizing a specific research area, preferably the one in which they intend to do their dissertation work. Their summary surveys that area and describes open and interesting research problems.
They describe why they chose these problems and indicate what direction their research may take in the future.
They prepare to display expertise on both the topic presented and on any related material that the committee thinks is relevant.
The student should talk (at least briefly) about any research progress to date (e.g., MS project, PhD research, or class project). Some evidence of the ability to do research is expected.
The committee shall evaluate students on the basis of their comprehension of the fundamental facts and principles that apply within their research area and the student’s ability to think incisively and critically about the theoretical and practical aspects of this field.
Students must demonstrate command of the content and the ability to design and produce an acceptable dissertation.

Format B

This option includes the presentation and defense of a thesis proposal in addition to the requirements of format A. It will include a summary of research to date and plans for future work (or at least the next stage thereof). The committee shall not only evaluate the student's thesis proposal and his/her progress to date, but shall also evaluate according to format A. As in format A, the student should prepare a single document and presentation, but in this case additional emphasis must be placed on research completed to date and plans for the remainder of the dissertation research.

Thesis Proposal Defense

Students not presenting a satisfactory thesis proposal defense, either because they took format A for the QE or because the material presented in an format B exam was not deemed a satisfactory proposal defense (although it may have sufficed to pass the QE), must write up and present a thesis proposal, which should include a summary of the research to date and plans for the remainder of the dissertation research. They should be prepared to discuss background and related areas, but the focus of the proposal should be on the progress made so far, and detailed plans for completing the thesis. The standard for continuing on with PhD research is that the proposal has sufficient merit to lead to a satisfactory dissertation. Another purpose of this presentation is for faculty to provide feedback on the quality of work to date. For this step, the committee should consist of at least three members from EECS familiar with the research area, preferably including those on the dissertation committee.

Normative Time in Candidacy

Advancement to Candidacy

Students must file the advancement form in the Graduate Office no later than the end of the semester following the one in which the qualifying exam was passed. In approving this application, Graduate Division approves the dissertation committee and will send a certificate of candidacy.

Students in the EECS department are required to be in advanced to candidacy status at least two semesters before they are eligible to graduate. Once a student is advanced to candidacy, candidacy is valid for five years. For the first three years, non-resident tuition may be waived, if applicable.

Dissertation Talk

As part of the requirements for the doctoral degree, students must give a public talk on the research covered by their dissertation. The dissertation talk should be given a few months before the signing of the final submission of the dissertation. It must be given before the final submission of the dissertation. The talk should cover all the major components of the dissertation work in a substantial manner; in particular, the dissertation talk should not omit topics that will appear in the dissertation but are incomplete at the time of the talk.

The dissertation talk is to be attended by the whole dissertation committee, or, if this is not possible, by at least a majority of the members. Attendance at this talk is part of the committee's responsibility. It is, however, the responsibility of the student to schedule a time for the talk that is convenient for members of the committee. The EECS department requires that the talk be given during either the fall or spring semester.

Required Professional Development

Graduate Student Instructor Teaching Requirement

The department requires all PhD candidates to serve as graduate student instructors (GSIs) within the EECS department. The GSI teaching requirement not only helps to develop a student's communication skills, but it also makes a great contribution to the department's academic community. Students must fulfill this requirement by working as a GSI (excluding EL ENG 375 or COMPSCI 375) for a total of 30 hours minimum prior to graduation. At least 20 of those hours must be for an EE or CS undergraduate course.

Master's Degree Requirements

Unit requirements

A minimum of 24 units is required.

Curriculum

All courses must be taken for a letter grade, except courses numbered 299s, which are only offered for S/U credit.

Students must maintain a minimum cumulative GPA of 3.0. No credit will be given for courses in which the student earns a grade of D+ or below.

Transfer credit may be awarded for a maximum of four semester or six quarter units of graduate coursework from another institution.

Plan I

Course List
Code	Title	Units
10 units of courses, selected from the 200-series (excluding 298 and 299) in EECS
EL ENG 299	Individual Research	4-10
or COMPSCI 299	Individual Research
Upper division or graduate courses to reach the minimum of 24 units

Plan II

Course List
Code	Title	Units
10 units of courses, selected from the 200-series (excluding 298 and 299) in EECS
EL ENG 299	Individual Research	3-6
or COMPSCI 299	Individual Research
Upper division or graduate courses to reach the minimum of 24 units

Advancement to Candidacy

For both Plan I and Plan II MS students, students need to complete the departmental Advance to Candidacy form, have their research advisor sign the form, and submit the form to the department. Once a student is advanced to candidacy, candidacy is valid for three years.

Capstone/Thesis (Plan I)

Students planning to use Plan I for their MS Degree will need to follow the Graduate Division's “Thesis Filing Guidelines." They will also need to complete the Graduate Division Advance to Candidacy form and submit this to the department no later than the end of the second week of classes of their final semester.

Capstone/Master's Project (Plan II)

Students planning to use Plan II for their MS Degree will need to produce an MS Plan II Title/Signature Page. A copy of the signature page and abstract should be submitted to the EECS Graduate Student Services staff. There is no special formatting required for the body of the Plan II MS report unlike the Plan I MS thesis which must follow strict Graduate Division guidelines.

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, 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 [-]

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: 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

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.

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

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.

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: Fall 2020, Spring 2020, Fall 2019, Spring 2019
Explores the data science lifecycle: question formulation, data collection and cleaning, exploratory, analysis, visualization, statistical inference, prediction, and decision-making. Focuses on quantitative critical thinking and key principles and techniques: languages for transforming, querying and analyzing data; algorithms for machine learning methods: regression, classification and clustering; principles of informative visualization; measurement error and prediction; and techniques for scalable data processing. Research term project.
Principles and Techniques of Data Science: Read More [+]

Rules & Requirements

Prerequisites: Computer Science/Information/Statistics C8 or Engineering 7; and either Computer Science 61A or Computer Science 88. Corequisite: Mathematics 54 or Electrical Engineering 16A

Credit Restrictions: Students will receive no credit for STAT C200C\COMPSCI C200A\DATA C200 after completing DATA C100, or STAT 200C. A deficient grade in STAT C200C\COMPSCI C200A\DATA C200 may be removed by taking STAT 200C.

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: Computer Science/Graduate

Grading: Letter grade.

Formerly known as: Statistics C200C/Computer Science C200A

Also listed as: DATA C200/STAT C200C

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

Terms offered: Fall 2020, Fall 2019, Fall 2018
This course introduces students to the basics of models, analysis tools, and control for embedded systems operating in real time. Students learn how to combine physical processes with computation. Topics include models of computation, control, analysis and verification, interfacing with the physical world, 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 [+]

Rules & Requirements

Credit Restrictions: Students will receive no credit for Electrical Engineering/Computer Science C249A after completing Electrical Engineering/Computer Science C149.

Hours & Format

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

Additional Details

Subject/Course Level: Computer Science/Graduate

Grading: Letter grade.

Instructors: Lee, Seshia

Formerly known as: Electrical Engineering C249M/Computer Science C249M

Also listed as: EL ENG C249A

Introduction to Embedded Systems: Read Less [-]

Terms offered: Fall 2020, Spring 2017, Spring 2016
Unified top-down and bottom-up design of integrated circuits and systems concentrating on architectural and topological issues. VLSI architectures, systolic arrays, self-timed systems. Trends in VLSI development. Physical limits. Tradeoffs in custom-design, standard cells, gate arrays. VLSI design tools.
VLSI Systems Design: Read More [+]

Rules & Requirements

Prerequisites: 150

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 4 hours of laboratory per week

Additional Details

Subject/Course Level: Computer Science/Graduate

Grading: Letter grade.

Instructor: Wawrzynek

VLSI Systems Design: Read Less [-]

Terms offered: Spring 2020, Spring 2019, Spring 2018
Graduate survey of contemporary computer organizations covering: early systems, CPU design, instruction sets, control, processors, busses, ALU, memory, I/O interfaces, connection networks, virtual memory, pipelined computers, multiprocessors, and case studies. Term paper or project is required.
Graduate Computer Architecture: Read More [+]

Rules & Requirements

Prerequisites: 152

Hours & Format

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

Additional Details

Subject/Course Level: Computer Science/Graduate

Grading: Letter grade.

Instructors: Asanović, Kubiatowicz

Graduate Computer Architecture: 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: Computer Science 61B, 61BL, or consent of instructor

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

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/Graduate

Grading: Letter grade.

Instructors: Agrawala, Canny, Hartmann

User Interface Design and Development: Read Less [-]

Terms offered: Fall 2017
This course is a broad introduction to conducting research in Human-Computer Interaction. Students will become familiar with seminal and recent literature; learn to review and critique research papers; re-implement and evaluate important existing systems; and gain experience in conducting research. Topics include input devices, computer-supported cooperative work, crowdsourcing, design tools, evaluation methods, search and mobile interfaces, usable security, help and tutorial systems.
Human-Computer Interaction Research: Read More [+]

Rules & Requirements

Prerequisites: Computer Science 160 recommended, or consent of instructor

Hours & Format

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

Additional Details

Subject/Course Level: Computer Science/Graduate

Grading: Letter grade.

Instructor: Hartmann

Human-Computer Interaction Research: Read Less [-]

Terms offered: Fall 2018, Fall 2017, Fall 2015
Graduate survey of modern topics in computer security, including protection, access control, distributed access security, firewalls, secure coding practices, safe languages, mobile code, and case studies from real-world systems. May also cover cryptographic protocols, privacy and anonymity, and/or other topics as time permits.
Security in Computer Systems: Read More [+]

Rules & Requirements

Prerequisites: 162

Hours & Format

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

Additional Details

Subject/Course Level: Computer Science/Graduate

Grading: Letter grade.

Instructors: D. Song, Wagner

Security in Computer Systems: Read Less [-]

Terms offered: Spring 2020, Fall 2016, Spring 2015
Develops a thorough grounding in Internet and network security suitable for those interested in conducting research in the area or those more broadly interested in security or networking. Potential topics include denial-of-service; capabilities; network intrusion detection/prevention; worms; forensics; scanning; traffic analysis; legal issues; web attacks; anonymity; wireless and networked devices; honeypots; botnets; scams; underground economy; attacker infrastructure; research pitfalls.
Internet and Network Security: Read More [+]

Rules & Requirements

Prerequisites: Electrical Engineering 122 or equivalent; Computer Science 161 or familiarity with basic security concepts

Hours & Format

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

Additional Details

Subject/Course Level: Computer Science/Graduate

Grading: Letter grade.

Instructor: Paxson

Internet and Network Security: Read Less [-]

Terms offered: Fall 2020, Fall 2019, Fall 2018
Graduate survey of systems for managing computation and information, covering a breadth of topics: early systems; volatile memory management, including virtual memory and buffer management; persistent memory systems, including both file systems and transactional storage managers; storage metadata, physical vs. logical naming, schemas, process scheduling, threading and concurrency control; system support for networking, including remote procedure calls, transactional RPC, TCP, and active messages; security infrastructure; extensible systems and APIs; performance analysis and engineering of large software systems. Homework assignments, exam, and term paper or project required.
Advanced Topics in Computer Systems: Read More [+]

Rules & Requirements

Prerequisites: 162 and entrance exam

Hours & Format

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

Additional Details

Subject/Course Level: Computer Science/Graduate

Grading: Letter grade.

Instructors: Brewer, Hellerstein

Formerly known as: 262

Advanced Topics in Computer Systems: Read Less [-]

Terms offered: Spring 2020, Spring 2009, Fall 2008
Continued graduate survey of large-scale systems for managing information and computation. Topics include basic performance measurement; extensibility, with attention to protection, security, and management of abstract data types; index structures, including support for concurrency and recovery; parallelism, including parallel architectures, query processing and scheduling; distributed data management, including distributed and mobile file systems and databases; distributed caching; large-scale data analysis and search. Homework assignments, exam, and term paper or project required.
Advanced Topics in Computer Systems: Read More [+]

Rules & Requirements

Prerequisites: 262A

Hours & Format

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

Additional Details

Subject/Course Level: Computer Science/Graduate

Grading: Letter grade.

Instructors: Brewer, Culler, Hellerstein, Joseph

Advanced Topics in Computer Systems: Read Less [-]

Terms offered: Fall 2019, Spring 2019, Spring 2014
Selected topics from: analysis, comparison, and design of programming languages, formal description of syntax and semantics, advanced programming techniques, structured programming, debugging, verification of programs and compilers, and proofs of correctness.
Design of Programming Languages: Read More [+]

Rules & Requirements

Prerequisites: 164

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/Graduate

Grading: Letter grade.

Instructor: Necula

Design of Programming Languages: Read Less [-]

Terms offered: Spring 2011, Spring 2010, Spring 2005
Compiler construction. Lexical analysis, syntax analysis. Semantic analysis code generation and optimization. Storage management. Run-time organization.
Implementation of Programming Languages: Read More [+]

Rules & Requirements

Prerequisites: 164, 263 recommended

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture, 1 hour of discussion, and 6 hours of laboratory per week

Additional Details

Subject/Course Level: Computer Science/Graduate

Grading: Letter grade.

Instructor: Bodik

Implementation of Programming Languages: Read Less [-]

Terms offered: Fall 2009, Spring 2003, Spring 2000
Table-driven and retargetable code generators. Register management. Flow analysis and global optimization methods. Code optimization for advanced languages and architectures. Local code improvement. Optimization by program transformation. Selected additional topics. A term paper or project is required.
Compiler Optimization and Code Generation: Read More [+]

Rules & Requirements

Prerequisites: 164

Hours & Format

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

Additional Details

Subject/Course Level: Computer Science/Graduate

Grading: Letter grade.

Instructor: Sen

Compiler Optimization and Code Generation: Read Less [-]

Terms offered: Spring 2020, Spring 2019, Spring 2018
Models for parallel programming. Overview of parallelism in scientific applications and study of parallel algorithms for linear algebra, particles, meshes, sorting, FFT, graphs, machine learning, etc. Survey of parallel machines and machine structures. Programming shared- and distributed-memory parallel computers, GPUs, and cloud platforms. Parallel programming languages, compilers, libraries and toolboxes. Data partitioning techniques. Techniques for synchronization and load balancing. Detailed study and algorithm/program development of medium sized applications.
Applications of Parallel Computers: Read More [+]

Rules & Requirements

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

Hours & Format

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

Additional Details

Subject/Course Level: Computer Science/Graduate

Grading: Letter grade.

Instructors: Demmel, Yelick

Also listed as: ENGIN C233

Applications of Parallel Computers: Read Less [-]

Terms offered: Prior to 2007
Parallel programming, from laptops to supercomputers to the cloud. Goals include writing programs that run fast while minimizing programming effort. Parallel architectures and programming languages and models, including shared memory (eg OpenMP on your multicore laptop), distributed memory (MPI and UPC on a supercomputer), GPUs (CUDA and OpenCL), and cloud (MapReduce, Hadoop and Spark). Parallel algorithms and software tools for common computations (eg dense and sparse linear algebra, graphs, structured grids). Tools for load balancing, performance analysis, debugging. How high level applications are built (eg climate modeling). On-line lectures and office hours.
Applications of Parallel Computers: Read More [+]

Objectives & Outcomes

Student Learning Outcomes: An understanding of computer architectures at a high level, in order to understand what can and cannot be done in parallel, and the relative costs of operations like arithmetic, moving data, etc.
To master parallel programming languages and models for different computer architectures
To recognize programming "patterns" to use the best available algorithms and software to implement them.
To understand sources of parallelism and locality in simulation in designing fast algorithms

Rules & Requirements

Prerequisites: Computer Science W266 or the consent of the instructor

Credit Restrictions: Students will receive no credit for Computer Science W267 after completing Computer Science C267.

Hours & Format

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

Online: This is an online course.

Additional Details

Subject/Course Level: Computer Science/Graduate

Grading: Letter grade.

Instructors: Demmel, Yelick

Applications of Parallel Computers: Read Less [-]

Terms offered: Spring 2019, Spring 2016, Spring 2015
Distributed systems, their notivations, applications, and organization. The network component. Network architectures. Local and long-haul networks, technologies, and topologies. Data link, network, and transport protocols. Point-to-point and broadcast networks. Routing and congestion control. Higher-level protocols. Naming. Internetworking. Examples and case studies.
Computer Networks: Read More [+]

Rules & Requirements

Prerequisites: 162

Hours & Format

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

Additional Details

Subject/Course Level: Computer Science/Graduate

Grading: Letter grade.

Instructors: Joseph, Katz, Stoica

Formerly known as: 292V

Computer Networks: Read Less [-]

Terms offered: Spring 2019, Spring 2017, Spring 2016
Design and analysis of efficient algorithms for combinatorial problems. Network flow theory, matching theory, matroid theory; augmenting-path algorithms; branch-and-bound algorithms; data structure techniques for efficient implementation of combinatorial algorithms; analysis of data structures; applications of data structure techniques to sorting, searching, and geometric problems.
Combinatorial Algorithms and Data Structures: Read More [+]

Rules & Requirements

Prerequisites: 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/Graduate

Grading: Letter grade.

Instructors: Papadimitriou, Rao, Sinclair, Vazirani

Combinatorial Algorithms and Data Structures: Read Less [-]

Terms offered: Spring 2020, Spring 2018, Fall 2011
Computational applications of randomness and computational theories of randomness. Approximate counting and uniform generation of combinatorial objects, rapid convergence of random walks on expander graphs, explicit construction of expander graphs, randomized reductions, Kolmogorov complexity, pseudo-random number generation, semi-random sources.
Randomness and Computation: Read More [+]

Rules & Requirements

Prerequisites: 170 and at least one course numbered 270-279

Hours & Format

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

Additional Details

Subject/Course Level: Computer Science/Graduate

Grading: Letter grade.

Instructor: Sinclair

Randomness and Computation: Read Less [-]

Terms offered: Spring 2012, Fall 2010, Spring 2009
. Fundamental theoretical issues in designing parallel algorithms and architectures. Shared memory models of parallel computation. Parallel algorithms for linear algegra, sorting, Fourier Transform, recurrence evaluation, and graph problems. Interconnection network based models. Algorithm design techniques for networks like hypercubes, shuffle-exchanges, threes, meshes and butterfly networks. Systolic arrays and techniques for generating them. Message routing.
Foundations of Parallel Computation: Read More [+]

Rules & Requirements

Prerequisites: 170, or consent of instructor

Hours & Format

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

Additional Details

Subject/Course Level: Computer Science/Graduate

Grading: Letter grade.

Instructor: Rao

Foundations of Parallel Computation: Read Less [-]

Terms offered: Spring 2019, Spring 2017, Spring 2015
. Constructive problems in computational geometry: convex hulls, triangulations, Voronoi diagrams, arrangements of hyperplanes; relationships among these problems. Search problems: advanced data structures; subdivision search; various kinds of range searches. Models of computation; lower bounds.
Computational Geometry: Read More [+]

Rules & Requirements

Prerequisites: 170 or equivalent

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

Hours & Format

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

Additional Details

Subject/Course Level: Computer Science/Graduate

Grading: Letter grade.

Instructor: Shewchuk

Computational Geometry: Read Less [-]

Terms offered: Fall 2020, Fall 2018, Fall 2017
Graduate survey of modern topics on theory, foundations, and applications of modern cryptography. One-way functions; pseudorandomness; encryption; authentication; public-key cryptosystems; notions of security. May also cover zero-knowledge proofs, multi-party cryptographic protocols, practical applications, and/or other topics, as time permits.
Cryptography: Read More [+]

Rules & Requirements

Prerequisites: 170

Hours & Format

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

Additional Details

Subject/Course Level: Computer Science/Graduate

Grading: Letter grade.

Instructors: Trevisan, Wagner

Cryptography: Read Less [-]

Terms offered: Spring 2020, Spring 2019, Spring 2018
Paradigms for computational vision. Relation to human visual perception. Mathematical techniques for representing and reasoning, with curves, surfaces and volumes. Illumination and reflectance models. Color perception. Image segmentation and aggregation. Methods for bottom-up three dimensional shape recovery: Line drawing analysis, stereo, shading, motion, texture. Use of object models for prediction and recognition.
Computer Vision: Read More [+]

Rules & Requirements

Prerequisites: Knowledge of linear algebra and calculus. Mathematics 1A-1B, 53, 54 or equivalent

Hours & Format

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

Additional Details

Subject/Course Level: Computer Science/Graduate

Grading: Letter grade.

Instructor: Malik

Also listed as: VIS SCI C280

Computer Vision: Read Less [-]

Terms offered: Fall 2020, Fall 2019, Fall 2016
Classification regression, clustering, dimensionality, reduction, and density estimation. Mixture models, hierarchical models, factorial models, hidden Markov, and state space models, Markov properties, and recursive algorithms for general probabilistic inference nonparametric methods including decision trees, kernal methods, neural networks, and wavelets. Ensemble methods.
Statistical Learning Theory: Read More [+]

Hours & Format

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

Additional Details

Subject/Course Level: Computer Science/Graduate

Grading: Letter grade.

Instructors: Bartlett, Jordan, Wainwright

Also listed as: STAT C241A

Statistical Learning Theory: Read Less [-]

Terms offered: Spring 2017, Spring 2016, Spring 2014
Recent topics include: Graphical models and approximate inference algorithms. Markov chain Monte Carlo, mean field and probability propagation methods. Model selection and stochastic realization. Bayesian information theoretic and structural risk minimization approaches. Markov decision processes and partially observable Markov decision processes. Reinforcement learning.
Advanced Topics in Learning and Decision Making: Read More [+]

Hours & Format

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

Additional Details

Subject/Course Level: Computer Science/Graduate

Grading: Letter grade.

Instructors: Bartlett, Jordan, Wainwright

Also listed as: STAT C241B

Advanced Topics in Learning and Decision Making: Read Less [-]

Terms offered: Spring 2020, 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 and MATH 54 or equivalent; COMPSCI 70 or STAT 134; COMPSCI 61B or equivalent; COMPSCI 189 or COMPSCI 289A (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/Graduate

Grading: Letter grade.

Instructor: Canny

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

Terms offered: Spring 2020, Spring 2019, Spring 2018
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.
Foundations of Computer Graphics: Read More [+]

Rules & Requirements

Prerequisites: Computer Science 61B or 61BL; programming skills in C, C++, or Java; linear algebra and calculus; or consent of instructor

Credit Restrictions: Students will receive no credit for Computer Science 284A after taking 184.

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/Graduate

Grading: Letter grade.

Instructors: Agrawala, Barsky, O'Brien, Ramamoorthi, Sequin

Foundations of Computer Graphics: Read Less [-]

Terms offered: Spring 2019, Spring 2017
This course provides a graduate-level introduction to advanced computer graphics algorithms and techniques. Students should already be familiar with basic concepts such as transformations, scan-conversion, scene graphs, shading, and light transport. Topics covered in this course include global illumination, mesh processing, subdivision surfaces, basic differential geometry, physically based animation, inverse kinematics, imaging and computational photography, and precomputed light transport.
Advanced Computer Graphics Algorithms and Techniques: Read More [+]

Rules & Requirements

Prerequisites: 184 or equivalent

Hours & Format

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

Additional Details

Subject/Course Level: Computer Science/Graduate

Grading: Letter grade.

Instructors: O'Brien, Ramamoorthi

Formerly known as: Computer Science 283

Advanced Computer Graphics Algorithms and Techniques: Read Less [-]

Terms offered: Fall 2020, Fall 2019, Fall 2011
Intersection of control, reinforcement learning, and deep learning. Deep learning methods, which train large parametric function approximators, achieve excellent results on problems that require reasoning about unstructured real-world situations (e.g., computer vision, speech recognition, NLP). Advanced treatment of the reinforcement learning formalism, the most critical model-free reinforcement learning algorithms (policy gradients, value function and Q-function learning, and actor-critic), a discussion of model-based reinforcement learning algorithms, an overview of imitation learning, and a range of advanced topics (e.g., exploration, model-based learning with video prediction, transfer learning, multi-task learning, and meta-learning).
Deep Reinforcement Learning, Decision Making, and Control: Read More [+]

Objectives & Outcomes

Student Learning Outcomes: Provide an opportunity to embark on a research-level final project with support from course staff.
Provide hands-on experience with several commonly used RL algorithms;
Provide students with an overview of advanced deep reinforcement learning topics, including current research trends;
Provide students with foundational knowledge to understand deep reinforcement learning algorithms;

Rules & Requirements

Prerequisites: COMPSCI 189 or COMPSCI 289A or equivalent

Hours & Format

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

Additional Details

Subject/Course Level: Computer Science/Graduate

Grading: Letter grade.

Instructors: Levine, Abbeel

Deep Reinforcement Learning, Decision Making, and Control: Read Less [-]

Terms offered: Spring 2018, Fall 2017, Spring 2017
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: Computer Science 61B and 61C

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

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/Graduate

Grading: Letter grade.

Instructors: Franklin, Hellerstein

Introduction to Database Systems: Read Less [-]

Terms offered: Spring 2020, Fall 2014
Implementation of data base systems on modern hardware systems. Considerations concerning operating system design, including buffering, page size, prefetching, etc. Query processing algorithms, design of crash recovery and concurrency control systems. Implementation of distributed data bases and data base machines.
Implementation of Data Base Systems: Read More [+]

Rules & Requirements

Prerequisites: Computer Science 162 and 186 or 286A

Hours & Format

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

Additional Details

Subject/Course Level: Computer Science/Graduate

Grading: Letter grade.

Instructors: Franklin, Hellerstein

Implementation of Data Base Systems: Read Less [-]

Terms offered: Fall 2019, Fall 2015, Spring 2015
Advanced topics related to current research in algorithms and artificial intelligence for robotics. Planning, control, and estimation for realistic robot systems, taking into account: dynamic constraints, control and sensing uncertainty, and non-holonomic motion constraints.
Advanced Robotics: Read More [+]

Rules & Requirements

Prerequisites: Instructor consent for undergraduate and masters students

Hours & Format

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

Additional Details

Subject/Course Level: Computer Science/Graduate

Grading: Letter grade.

Instructor: Abbeel

Advanced Robotics: Read Less [-]

Terms offered: Spring 2020
As robot autonomy advances, it becomes more and more important to develop algorithms that are not solely functional, but also mindful of the end-user. How should the robot move differently when it's moving in the presence of a human? How should it learn from user feedback? How should it assist the user in accomplishing day to day tasks? These are the questions we will investigate in this course.
We will contrast existing algorithms in robotics with studies in human-robot interaction, discussing how to tackle interaction challenges in an algorithmic way, with the goal of enabling generalization across robots and tasks. We will also sharpen research skills: giving good talks, experimental design, statistical analysis, literature surveys.
Algorithmic Human-Robot Interaction: Read More [+]

Objectives & Outcomes

Student Learning Outcomes: Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to
apply Bayesian inference and learning techniques to enhance coordination in collaborative tasks.
Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to
apply optimization techniques to generate motion for HRI.
Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to
contrast and relate model-based and model-free learning from demonstration.
Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to
develop a basic understanding of verbal and non-verbal communication.
Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to
ground algorithmic HRI in the relvant psychology background.
Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to
tease out the intricacies of developing algorithms that support HRI.
Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to analyze and diagram the literature related to a particular topic.
Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to communicate scientific content to a peer audience.
Students will have gained both knowledge/abilities related to human-robot interaction, as well as to research and presentation skills including being able to critique a scientific paper's experimental design and analysis.

Hours & Format

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

Additional Details

Subject/Course Level: Computer Science/Graduate

Grading: Letter grade.

Instructor: Dragan

Algorithmic Human-Robot Interaction: Read Less [-]

Terms offered: Spring 2020, Fall 2014, Spring 2013
Methods and models for the analysis of natural (human) language data. Topics include: language modeling, speech recognition, linguistic analysis (syntactic parsing, semantic analysis, reference resolution, discourse modeling), machine translation, information extraction, question answering, and computational linguistics techniques.
Natural Language Processing: Read More [+]

Rules & Requirements

Prerequisites: CS188 required, CS170 recommended

Hours & Format

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

Additional Details

Subject/Course Level: Computer Science/Graduate

Grading: Letter grade.

Instructor: Klein

Natural Language Processing: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
This course provides an introduction to theoretical foundations, algorithms, and methodologies for machine learning, emphasizing the role of probability and optimization and exploring a variety of real-world applications. Students are expected to have a solid foundation in calculus and linear algebra as well as exposure to the basic tools of logic and probability, and should be familiar with at least one modern, high-level programming language.
Introduction to Machine Learning: Read More [+]

Rules & Requirements

Prerequisites: Mathematics 53, 54; Computer Science 70; Computer Science 188 or consent of instructor

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

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/Graduate

Grading: Letter grade.

Instructors: Listgarten , Malik, Recht, Sahai, Shewchuk

Introduction to Machine Learning: Read Less [-]

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

Rules & Requirements

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

Hours & Format

Fall and/or spring:
4 weeks - 3-15 hours of lecture per week
6 weeks - 3-9 hours of lecture per week
8 weeks - 2-6 hours of lecture per week
10 weeks - 2-5 hours of lecture per week
15 weeks - 1-3 hours of lecture per week

Additional Details

Subject/Course Level: Computer Science/Graduate

Grading: Letter grade.

Special Topics: Read Less [-]

Terms offered: Spring 2016, Fall 2015, Summer 2015 10 Week Session
Supervised experience in off-campus companies relevant to specific aspects and applications of electrical engineering and/or computer science. Written report required at the end of the semester.
Field Studies in Computer Science: Read More [+]

Rules & Requirements

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

Hours & Format

Fall and/or spring: 15 weeks - 1-12 hours of independent study per week

Summer:
6 weeks - 1-30 hours of independent study per week
8 weeks - 1.5-22.5 hours of independent study per week
10 weeks - 1-18 hours of independent study per week

Additional Details

Subject/Course Level: Computer Science/Graduate

Grading: Offered for satisfactory/unsatisfactory grade only.

Field Studies in Computer Science: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
Advanced study in various subjects through seminars on topics to be selected each year, informal group studies of special problems, group participation in comprehensive design problems, or group research on complete problems for analysis and experimentation.
Group Studies Seminars, or Group Research: 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 lecture per week

Additional Details

Subject/Course Level: Computer Science/Graduate

Grading: The grading option will be decided by the instructor when the class is offered.

Group Studies Seminars, or Group Research: Read Less [-]

Terms offered: Summer 2017 Second 6 Week Session, Fall 2016, Summer 2016 10 Week Session
Investigations of problems in computer science.
Individual Research: Read More [+]

Rules & Requirements

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

Hours & Format

Fall and/or spring: 15 weeks - 0-1 hours of independent study per week

Summer:
6 weeks - 8-30 hours of independent study per week
8 weeks - 6-22.5 hours of independent study per week
10 weeks - 1.5-18 hours of independent study per week

Additional Details

Subject/Course Level: Computer Science/Graduate

Grading: Offered for satisfactory/unsatisfactory grade only.

Individual Research: Read Less [-]

Terms offered: Fall 2012, Fall 2011, Spring 2011
Supervised teaching practice, in either a one-on-one tutorial or classroom discussion setting.
Teaching Practice: Read More [+]

Rules & Requirements

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/Professional course for teachers or prospective teachers

Grading: Offered for satisfactory/unsatisfactory grade only.

Teaching Practice: Read Less [-]

Terms offered: Spring 2020, Spring 2014, Spring 2012
Discussion and review of research and practice relating to the teaching of computer science: knowledge organization and misconceptions, curriculum and topic organization, evaluation, collaborative learning, technology use, and administrative issues. As part of a semester-long project to design a computer science course, participants invent and refine a variety of homework and exam activities, and evaluate alternatives for textbooks, grading and other administrative policies, and innovative uses of technology.
Designing Computer Science Education: Read More [+]

Rules & Requirements

Prerequisites: Computer Science 301 and two semesters of GSI experience

Hours & Format

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

Additional Details

Subject/Course Level: Computer Science/Professional course for teachers or prospective teachers

Grading: Letter grade.

Instructor: Garcia

Designing Computer Science Education: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
This is a course for aspiring teachers or those who want to instruct with expertise from evidence-based research and proven equity-oriented practices. It provides pedagogical training by introducing the big ideas of teaching and learning, and illustrating how to put them into practice. The course is divided into three sections—instructing the individual; a group; and psycho-social factors that affect learning at any level. These sections are designed to enhance any intern’s, tutor’s, or TA’s teaching skillset. Class is discussion based, and covers theoretical and practical pedagogical aspects to teaching in STEM. An integral feature of the course involves providing weekly tutoring sessions.
Adaptive Instruction Methods in Computer Science: Read More [+]

Rules & Requirements

Prerequisites: Prerequisite satisfied Concurrently: experience tutoring or as an academic intern; or concurrently serving as an academic intern while taking course

Hours & Format

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

Additional Details

Subject/Course Level: Computer Science/Professional course for teachers or prospective teachers

Grading: Letter grade.

Instructor: Hunn

Adaptive Instruction Methods in Computer Science: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
Discussion and practice of techniques for effective teaching, focusing on issues most relevant to teaching assistants in computer science courses.
Teaching Techniques for Computer Science: Read More [+]

Rules & Requirements

Prerequisites: Consent of instructor

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

Hours & Format

Fall and/or spring: 10 weeks - 3 hours of discussion per week

Summer: 8 weeks - 4 hours of discussion per week

Additional Details

Subject/Course Level: Computer Science/Professional course for teachers or prospective teachers

Grading: Offered for satisfactory/unsatisfactory grade only.

Instructors: Barsky, Garcia, Harvey

Teaching Techniques for Computer Science: Read Less [-]

Terms offered: Spring 2020, Fall 2018, Fall 2016
Discussion, problem review and development, guidance of computer science laboratory sections, course development, supervised practice teaching.
Professional Preparation: Supervised Teaching of Computer Science: Read More [+]

Rules & Requirements

Prerequisites: Appointment as graduate student instructor

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

Hours & Format

Fall and/or spring: 15 weeks - 1-2 hours of independent study per week

Summer: 8 weeks - 1-2 hours of independent study per week

Additional Details

Subject/Course Level: Computer Science/Professional course for teachers or prospective teachers

Grading: Offered for satisfactory/unsatisfactory grade only.

Professional Preparation: Supervised Teaching of Computer Science: Read Less [-]

Terms offered: Fall 2015, Fall 2014, Spring 2014
Individual study in consultation with the major field adviser, intended to provide an opportunity for qualified students to prepare themselves for the various examinations required of candidates for the Ph.D. (and other doctoral degrees).
Individual Study for Doctoral Students: Read More [+]

Rules & Requirements

Credit Restrictions: Course does not satisfy unit or residence requirements for doctoral degree.

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: 8 weeks - 6-45 hours of independent study per week

Additional Details

Subject/Course Level: Computer Science/Graduate examination preparation

Grading: Offered for satisfactory/unsatisfactory grade only.

Individual Study for Doctoral Students: Read Less [-]

Electrical Engineering

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 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: 120 or equivalent, or consent of instructor

Credit Restrictions: Students will receive no credit for 206A after taking C125/Bioengineering C125 or EE 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 Engineering/Graduate

Grading: Letter grade.

Instructor: Bajcsy

Formerly known as: Electrical Engineering 215A

Introduction to Robotics: Read Less [-]

Terms offered: Spring 2018, Spring 2017
This course is a sequel to EECS 125/225, 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 [+]

Objectives & Outcomes

Course Objectives: To teach students the connection between the geometry, physics of
manipulators with experimental setups that include sensors, control of large degrees of freedom
manipulators, mobile robots and different grippers.

Student Learning Outcomes: By the end of the course students will be able to build a complete
system composed of perceptual planning and autonomously controlled manipulators and /or
mobile systems, justified by predictive theoretical models of performance.

Rules & Requirements

Prerequisites: Electrical Engineering 206A/Bioengineering 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/Graduate

Grading: Letter grade.

Instructors: Bajcsy, Sastry

Robotic Manipulation and Interaction: Read Less [-]

Terms offered: Spring 2011, Spring 2010, Fall 2006
Advanced treatment of classical electromagnetic theory with engineering applications. Boundary value problems in electrostatics. Applications of Maxwell's Equations to the study of waveguides, resonant cavities, optical fiber guides, Gaussian optics, diffraction, scattering, and antennas.
Applied Electromagnetic Theory: Read More [+]

Rules & Requirements

Prerequisites: 117, or Physics 110A, 110B

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Formerly known as: 210A-210B

Applied Electromagnetic Theory: Read Less [-]

Terms offered: Fall 2020, Fall 2019, Spring 2019
Power conversion circuits and techniques. Characterization and design of magnetic devices including transformers, inductors, and electromagnetic actuators. Characteristics of power semiconductor devices, including power diodes, SCRs, MOSFETs, IGBTs, and emerging wide bandgap devices. Applications to renewable energy systems, high-efficiency lighting, power management in mobile electronics, and electric machine drives. Simulation based laboratory and design project.
Power Electronics: Read More [+]

Rules & Requirements

Prerequisites: EE105 or consent of instructor

Credit Restrictions: Students who have received credit for EE113 will not receive credit for EE213A.

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/Graduate

Grading: Letter grade.

Instructor: Sanders

Power Electronics: Read Less [-]

Terms offered: Fall 2019, Fall 2017, Fall 2016
This course explores modern developments in the physics and applications of x-rays and extreme ultraviolet (EUV) radiation. It begins with a review of electromagnetic radiation at short wavelengths including dipole radiation, scattering and refractive index, using a semi-classical atomic model. Subject matter includes the generation of x-rays with synchrotron radiation, high harmonic generation, x-ray free electron lasers, laser-plasma sources. Spatial and temporal coherence concepts are explained. Optics appropriate for this spectral region are described. Applications include nanoscale and astrophysical imaging, femtosecond and attosecond probing of electron dynamics in molecules and solids, EUV lithography, and materials characteristics.
X-rays and Extreme Ultraviolet Radiation: Read More [+]

Rules & Requirements

Prerequisites: Physics 110, 137, and Mathematics 53, 54 or equivalent

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Instructor: Attwood

Also listed as: AST C210

X-rays and Extreme Ultraviolet Radiation: 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 218A after taking Electrical Engineering 118 or 119.

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/Graduate

Grading: Letter grade.

Instructors: Waller, Kante

Introduction to Optical Engineering: Read Less [-]

Terms offered: Fall 2019, Fall 2017, Fall 2015
Numerical simulation and modeling are enabling technologies that pervade science and engineering. This course provides a detailed introduction to the fundamental principles of these technologies and their translation to engineering practice. The course emphasizes hands-on programming in MATLAB and application to several domains, including circuits, nanotechnology, and biology.
Numerical Simulation and Modeling: Read More [+]

Rules & Requirements

Prerequisites: Consent of instructor; a course in linear algebra and on circuits is very useful

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Instructor: Roychowdhury

Numerical Simulation and Modeling: Read Less [-]

Terms offered: Spring 2016, Spring 2015, Spring 2011
The course covers the fundamental techniques for the design and analysis of digital circuits. The goal is to provide a detailed understanding of basic logic synthesis and analysis algorithms, and to enable students to apply this knowledge in the design of digital systems and EDA tools. The course will present combinational circuit optimization (two-level and multi-level synthesis), sequential circuit optimization (state encoding, retiming), timing analysis, testing, and logic verification.
Logic Synthesis: Read More [+]

Rules & Requirements

Prerequisites: 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/Graduate

Grading: Letter grade.

Logic Synthesis: Read Less [-]

Terms offered: Fall 2020, Fall 2019, Fall 2018
Input-output and state space representation of linear continuous and discrete time dynamic systems. Controllability, observability, and stability. Modeling and identification. Design and analysis of single and multi-variable feedback control systems in transform and time domain. State observer. Feedforward/preview control. Application to engineering systems.
Advanced Control Systems I: Read More [+]

Rules & Requirements

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

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/Graduate

Grading: Letter grade.

Instructors: Borrelli, Horowitz, Tomizuka, Tomlin

Also listed as: MEC ENG C232

Advanced Control Systems I: Read Less [-]

Terms offered: Fall 2020, Fall 2019, Fall 2018
Experience-based learning in the design of SISO and MIMO feedback controllers for linear systems. The student will master skills needed to apply linear control design and analysis tools to classical and modern control problems. In particular, the participant will be exposed to and develop expertise in two key control design technologies: frequency-domain control synthesis and time-domain optimization-based approach.
Experiential Advanced Control Design I: Read More [+]

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Also listed as: MEC ENG C231A

Experiential Advanced Control Design I: Read Less [-]

Terms offered: Spring 2020, Spring 2019, Spring 2018
Experience-based learning in the design, analysis, and verification of automatic control systems. The course emphasizes the use of computer-aided design techniques through case studies and design tasks. The student will master skills needed to apply advanced model-based control analysis, design, and estimation to a variety of industrial applications. The role of these specific design methodologies within the larger endeavor of control design is also addressed.
Experiential Advanced Control Design II: Read More [+]

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Also listed as: MEC ENG C231B

Experiential Advanced Control Design II: Read Less [-]

Terms offered: Prior to 2007
Introduction to input/output concepts from control theory, systems as operators in signal spaces, passivity and
small-gain theorems, dissipativity theory, integral quadratic constraints. Compositional stabilility and
performance certification for interconnected systems from subsystems input/output properties. Case studies in
multi-agent systems, biological networks, Internet congestion control, and adaptive control.
Input/Output Methods for Compositional System Analysis: Read More [+]

Objectives & Outcomes

Course Objectives: Standard computational tools for control synthesis and verification do not scale well to large-scale, networked
systems in emerging applications. This course presents a compositional methodology suitable when the
subsystems are amenable to analytical and computational methods but the interconnection, taken as a whole, is
beyond the reach of these methods. The main idea is to break up the task of certifying desired stability and
performance properties into subproblems of manageable size using input/output properties. Students learn about
the fundamental theory, as well as relevant algorithms and applications in several domains.

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Instructors: Arcak, Packard

Also listed as: MEC ENG C220D

Input/Output Methods for Compositional System Analysis: Read Less [-]

Terms offered: Fall 2020, Fall 2019, Fall 2018
Basic system concepts; state-space and I/O representation. Properties of linear systems. Controllability, observability, minimality, state and output-feedback. Stability. Observers. Characteristic polynomial. Nyquist test.
Linear System Theory: Read More [+]

Rules & Requirements

Prerequisites: 120; Mathematics 110 recommended

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Linear System Theory: Read Less [-]

Terms offered: Spring 2017, Spring 2016, Spring 2015
Basic graduate course in non-linear systems. Second Order systems. Numerical solution methods, the describing function method, linearization. Stability - direct and indirect methods of Lyapunov. Applications to the Lure problem - Popov, circle criterion. Input-Output stability. Additional topics include: bifurcations of dynamical systems, introduction to the "geometric" theory of control for nonlinear systems, passivity concepts and dissipative dynamical systems.
Nonlinear Systems--Analysis, Stability and Control: Read More [+]

Rules & Requirements

Prerequisites: 221A (may be taken concurrently)

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Nonlinear Systems--Analysis, Stability and Control: Read Less [-]

Terms offered: Spring 2020, Spring 2019, Spring 2018
Basic graduate course in nonlinear systems. Nonlinear phenomena, planar systems, bifurcations, center manifolds, existence and uniqueness theorems. Lyapunov’s direct and indirect methods, Lyapunov-based feedback stabilization. Input-to-state and input-output stability, and dissipativity theory. Computation techniques for nonlinear system analysis and design. Feedback linearization and sliding mode control methods.
Nonlinear Systems: Read More [+]

Rules & Requirements

Prerequisites: Math 54, or equivalent (undergraduate level Ordinary Differential Equations and Linear Algebra)

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Instructors: Arcak, Tomlin, Kameshwar

Also listed as: MEC ENG C237

Nonlinear Systems: Read Less [-]

Terms offered: Spring 2020, Spring 2018, Spring 2015
Parameter and state estimation. System identification. Nonlinear filtering. Stochastic control. Adaptive control.
Stochastic Systems: Estimation and Control: Read More [+]

Rules & Requirements

Prerequisites: 226A (which students are encouraged to take concurrently)

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Stochastic Systems: Estimation and Control: Read Less [-]

Terms offered: Fall 2010, Fall 2009, Fall 2008
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; channel equalization; information theoretic techniques; block, convolutional, and trellis coding techniques; multiuser communications and spread spectrum; multi-carrier techniques and FDM; carrier and symbol synchronization. Applications to design of digital telephone modems, compact disks, and digital wireless communication systems are illustrated. The concepts are illustrated by a sequence of MATLAB exercises.
Digital Communications: Read More [+]

Rules & Requirements

Prerequisites: 120 and 126, or equivalent

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Formerly known as: 224

Digital Communications: Read Less [-]

Terms offered: Spring 2013, Spring 2012, Spring 2010
Introduction of the fundamentals of wireless communication. Modeling of the wireless multipath fading channel and its basic physical parameters. Coherent and noncoherent reception. Diversity techniques over time, frequency, and space. Spread spectrum communication. Multiple access and interference management in wireless networks. Frequency re-use, sectorization. Multiple access techniques: TDMA, CDMA, OFDM. Capacity of wireless channels. Opportunistic communication. Multiple antenna systems: spatial multiplexing, space-time codes. Examples from existing wireless standards.
Fundamentals of Wireless Communication: Read More [+]

Rules & Requirements

Prerequisites: 121, 226A, or equivalent

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Instructor: Tse

Fundamentals of Wireless Communication: Read Less [-]

Terms offered: Spring 2014, Spring 2012, Spring 2009
Introduction to relevant signal processing and basics of pattern recognition. Introduction to coding, synthesis, and recognition. Models of speech and music production and perception. Signal processing for speech analysis. Pitch perception and auditory spectral analysis with applications to speech and music. Vocoders and music synthesizers. Statistical speech recognition, including introduction to Hidden Markov Model and Neural Network approaches.
Audio Signal Processing in Humans and Machines: Read More [+]

Rules & Requirements

Prerequisites: 123 or equivalent; Statistics 200A or equivalent; or graduate standing and consent of instructor

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Instructor: Morgan

Audio Signal Processing in Humans and Machines: Read Less [-]

Terms offered: Spring 2020, Spring 2019, Spring 2018, Spring 2017, Spring 2016
Fundamentals of MRI including signal-to-noise ratio, resolution, and contrast as dictated by physics, pulse sequences, and instrumentation. Image reconstruction via 2D FFT methods. Fast imaging reconstruction via convolution-back projection and gridding methods and FFTs. Hardware for modern MRI scanners including main field, gradient fields, RF coils, and shim supplies. Software for MRI including imaging methods such as 2D FT, RARE, SSFP, spiral and echo planar imaging methods.
Principles of Magnetic Resonance Imaging: Read More [+]

Objectives & Outcomes

Course Objectives: Graduate level understanding of physics, hardware, and systems engineering description of image formation, and image reconstruction in MRI. Experience in Imaging with different MR Imaging systems. This course should enable students to begin graduate level research at Berkeley (Neuroscience labs, EECS and Bioengineering), LBNL or at UCSF (Radiology and Bioengineering) at an advanced level and make research-level contribution

Rules & Requirements

Prerequisites: Either Electrical Engineering 120 or Bioengineering C165/Electrical Engineering C145B or consent of instructor

Credit Restrictions: Students will receive no credit for Bioengineering C265/El Engineering C225E after taking El Engineering 265.

Repeat rules: Course may be repeated for credit under special circumstances: Students can only receive credit for 1 of the 2 versions of the class,BioEc265 or EE c225e, not both

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/Graduate

Grading: Letter grade.

Instructors: Lustig, Conolly, Vandsburger

Also listed as: BIO ENG C265

Principles of Magnetic Resonance Imaging: Read Less [-]

Terms offered: Fall 2020, Fall 2019, Fall 2018
Probability, random variables and their convergence, random processes. Filtering of wide sense stationary processes, spectral density, Wiener and Kalman filters. Markov processes and Markov chains. Gaussian, birth and death, poisson and shot noise processes. Elementary queueing analysis. Detection of signals in Gaussian and shot noise, elementary parameter estimation.
Random Processes in Systems: Read More [+]

Rules & Requirements

Prerequisites: 120 and Statistics 200A or equivalent

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/Graduate

Grading: Letter grade.

Instructor: Anantharam

Formerly known as: 226

Random Processes in Systems: Read Less [-]

Terms offered: Spring 2017, Spring 2013, Spring 1997
Advanced topics such as: Martingale theory, stochastic calculus, random fields, queueing networks, stochastic control.
Applications of Stochastic Process Theory: Read More [+]

Rules & Requirements

Prerequisites: 226A

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

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Instructors: Anantharam, Varaiya

Applications of Stochastic Process Theory: Read Less [-]

Terms offered: Fall 2020, Fall 2019, Fall 2018
Convex optimization is a class of nonlinear optimization problems where the objective to be minimized, and the constraints, are both convex. The course covers some convex optimization theory and algorithms, and describes various applications arising in engineering design, machine learning and statistics, finance, and operations research. The course includes laboratory assignments, which consist of hands-on experiments with the optimization software CVX, and a discussion section.
Convex Optimization: Read More [+]

Rules & Requirements

Prerequisites: Mathematics 54 and Statistics 2 or equivalents

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/Graduate

Grading: Letter grade.

Instructors: El Ghaoui, Wainwright

Convex Optimization: Read Less [-]

Terms offered: Spring 2020, Spring 2019, Spring 2018, Spring 2017
Convex optimization as a systematic approximation tool for hard decision problems. Approximations of combinatorial optimization problems, of stochastic programming problems, of robust optimization problems (i.e., with optimization problems with unknown but bounded data), of optimal control problems. Quality estimates of the resulting approximation. Applications in robust engineering design, statistics, control, finance, data mining, operations research.
Convex Optimization and Approximation: Read More [+]

Rules & Requirements

Prerequisites: 227A or consent of instructor

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Instructor: El Ghaoui

Also listed as: IND ENG C227B

Convex Optimization and Approximation: Read Less [-]

Terms offered: Prior to 2007
The course covers some convex optimization theory and algorithms, and describes various applications arising in engineering design, machine learning and statistics, finance, and operations research. The course includes laboratory assignments, which consist of hands-on experience.
Introduction to Convex Optimization: Read More [+]

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/Graduate

Grading: Letter grade.

Instructors: El Ghaoui, Wainwright

Formerly known as: Electrical Engineering C227A/Industrial Engin and Oper Research C227A

Also listed as: IND ENG C227A

Introduction to Convex Optimization: Read Less [-]

Terms offered: Fall 2014, Spring 2014, Fall 2011
Descriptions, models, and approaches to the design and management of networks. Optical transmission and switching technologies are described and analyzed using deterministic, stochastic, and simulation models. FDDI, DQDB, SMDS, Frame Relay, ATM, networks, and SONET. Applications demanding high-speed communication.
High Speed Communications Networks: Read More [+]

Rules & Requirements

Prerequisites: 122, 226A (may be taken concurrently)

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

High Speed Communications Networks: Read Less [-]

Terms offered: Fall 2020, Fall 2019, Fall 2018
Fundamental bounds of Shannon theory and their application. Source and channel coding theorems. Galois field theory, algebraic error-correction codes. Private and public-key cryptographic systems.
Information Theory and Coding: Read More [+]

Rules & Requirements

Prerequisites: 226 recommended, Statistics 200A or equivalent

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Instructors: Anantharam, Tse

Formerly known as: 229

Information Theory and Coding: Read Less [-]

Terms offered: Spring 2019, Spring 2016, Fall 2013
Error control codes are an integral part of most communication and recording systems where they are primarily used to provide resiliency to noise. In this course, we will cover the basics of error control coding for reliable digital transmission and storage. We will discuss the major classes of codes that are important in practice, including Reed Muller codes, cyclic codes, Reed Solomon codes, convolutional codes, concatenated codes, turbo codes, and low density parity check codes. The relevant background material from finite field and polynomial algebra will be developed as part of the course. Overview of topics: binary linear block codes; Reed Muller codes; Galois fields; linear block codes over a finite field; cyclic codes; BCH and Reed Solomon codes; convolutional codes and trellis based decoding, message passing decoding algorithms; trellis based soft decision decoding of block codes; turbo codes; low density parity check codes.
Error Control Coding: Read More [+]

Rules & Requirements

Prerequisites: 126 or equivalent (some familiarity with basic probability). Prior exposure to information theory not necessary

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Instructor: Anatharam

Error Control Coding: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
Overview of electronic properties of semiconductors. 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: 40 or 100

Credit Restrictions: Students will receive no credit for Electrical Engineering 230A after taking Electrical Engineering 130.

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/Graduate

Grading: Letter grade.

Formerly known as: Electrical Engineering 230M

Integrated-Circuit Devices: Read Less [-]

Terms offered: Fall 2020, Spring 2019, Spring 2018
Physical principles and operational characteristics of semiconductor devices. Emphasis is on MOS field-effect transistors and their behaviors dictated by present and probable future technologies. Metal-oxide-semiconductor systems, short-channel and high field effects, device modeling, and impact on analog, digital circuits.
Solid State Devices: Read More [+]

Rules & Requirements

Prerequisites: 130 or equivalent

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/Graduate

Grading: Letter grade.

Instructors: Subramanian, King Liu, Salahuddin

Formerly known as: Electrical Engineering 231

Solid State Devices: Read Less [-]

Terms offered: Fall 2018, Fall 2017, Fall 2016
Crystal structure and symmetries. Energy-band theory. Cyclotron resonance. Tensor effective mass. Statistics of electronic state population. Recombination theory. Carrier transport theory. Interface properties. Optical processes and properties.
Solid State Electronics: Read More [+]

Rules & Requirements

Prerequisites: 131; Physics 137B

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Instructors: Bokor, Salahuddin

Formerly known as: Electrical Engineering 230

Solid State Electronics: Read Less [-]

Terms offered: Spring 2019, Spring 2018, Spring 2017
Overview of electronic properties of semiconductors. 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: MAS-IC students only

Credit Restrictions: Students will receive no credit for Electrical Engineering W230A after taking Electrical Engineering 130, Electrical Engineering W130 or Electrical Engineering 230A.

Hours & Format

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

Summer: 10 weeks - 4.5 hours of web-based lecture and 1.5 hours of web-based discussion per week

Online: This is an online course.

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Instructors: Javey, Subramanian, King Liu

Formerly known as: Electrical Engineering W130

Integrated-Circuit Devices: Read Less [-]

Terms offered: Fall 2015
Physical principles and operational characteristics of semiconductor devices. Emphasis is on MOS field-effect transistors and their behaviors dictated by present and probable future technologies. Metal-oxide-semiconductor systems, short-channel and high field effects, device modeling, and impact on analog, digital circuits.
Solid State Devices: Read More [+]

Rules & Requirements

Prerequisites: EE W230A or equivalent; MAS-IC students only

Credit Restrictions: Students will receive no credit for EE W230B after taking EE 230B.

Hours & Format

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

Summer: 10 weeks - 4.5 hours of web-based lecture and 1.5 hours of web-based discussion per week

Online: This is an online course.

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Instructors: Subramanian, King Liu, Salahuddin

Formerly known as: Electrical Engineering W231

Solid State Devices: Read Less [-]

Terms offered: Spring 2020, Spring 2019, Spring 2018
This course is designed to give an introduction and overview of the fundamentals of optoelectronic devices. Topics such as optical gain and absorption spectra, quantization effects, strained quantum wells, optical waveguiding and coupling, and hetero p-n junction will be covered. This course will focus on basic physics and design principles of semiconductor diode lasers, light emitting diodes, photodetectors and integrated optics. Practical applications of the devices will be also discussed.
Lightwave Devices: Read More [+]

Rules & Requirements

Prerequisites: Electrical Engineering 130 or equivalent; Physics 137A and Electrical Engineering 117 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: Electrical Engineering/Graduate

Grading: Letter grade.

Instructor: Wu

Lightwave Devices: Read Less [-]

Terms offered: Spring 2016, Spring 2015, Spring 2013
This course discusses various top-down and bottom-up approaches to synthesizing and processing nanostructured materials. The topics include fundamentals of self assembly, nano-imprint lithography, electron beam lithography, nanowire and nanotube synthesis, quantum dot synthesis (strain patterned and colloidal), postsynthesis modification (oxidation, doping, diffusion, surface interactions, and etching techniques). In addition, techniques to bridging length scales such as heterogeneous integration will be discussed. We will discuss new electronic, optical, thermal, mechanical, and chemical properties brought forth by the very small sizes.
Nanoscale Fabrication: 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: Electrical Engineering/Graduate

Grading: Letter grade.

Instructor: Chang-Hasnain

Also listed as: NSE C203

Nanoscale Fabrication: Read Less [-]

Terms offered: Fall 2019, Fall 2017, Fall 2015
Interaction of radiation with atomic and semiconductor systems, density matrix treatment, semiclassical laser theory (Lamb's), laser resonators, specific laser systems, laser dynamics, Q-switching and mode-locking, noise in lasers and optical amplifiers. Nonlinear optics, phase-conjugation, electrooptics, acoustooptics and magnetooptics, coherent optics, stimulated Raman and Brillouin scattering.
Quantum and Optical Electronics: Read More [+]

Rules & Requirements

Prerequisites: 117A, Physics 137A or equivalent

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Quantum and Optical Electronics: Read Less [-]

Terms offered: Spring 2010, Spring 2009, Spring 2007
Introduction to partially ionized, chemically reactive plasmas, including collisional processes, diffusion, sources, sheaths, boundaries, and diagnostics. DC, RF, and microwave discharges. Applications to plasma-assisted materials processing and to plasma wall interactions.
Partially Ionized Plasmas: Read More [+]

Rules & Requirements

Prerequisites: An upper division course in electromagnetics or fluid dynamics

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Formerly known as: 239

Also listed as: AST C239

Partially Ionized Plasmas: 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.
Analog Integrated Circuits: Read More [+]

Rules & Requirements

Prerequisites: Electrical Engineering 105

Credit Restrictions: Students will receive no credit for Electrical Engineering 240A after taking El ectrical Engineering 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/Graduate

Grading: Letter grade.

Instructors: Sanders, Nguyen

Analog Integrated Circuits: Read Less [-]

Terms offered: Spring 2019, Spring 2018, Spring 2017
Analysis and optimized design of monolithic operational amplifiers and wide-band amplifiers; methods of achieving wide-band amplification, gain-bandwidth considerations; analysis of noise in integrated circuits and low noise design. Precision passive elements, analog switches, amplifiers and comparators, voltage reference in NMOS and CMOS circuits, Serial, successive-approximation, and parallel analog-to-digital converters. Switched-capacitor and CCD filters. Applications to codecs, modems.
Advanced Analog Integrated Circuits: Read More [+]

Rules & Requirements

Prerequisites: EE140/EE240A or equivalent

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/Graduate

Grading: Letter grade.

Advanced Analog Integrated Circuits: Read Less [-]

Terms offered: Fall 2019, Fall 2017, Fall 2015
Architectural and circuit level design and analysis of integrated analog-to-digital and digital-to-analog interfaces in CMOS and BiCMOS VLSI technology. Analog-digital converters, digital-analog converters, sample/hold amplifiers, continuous and switched-capacitor filters. RF integrated electronics including synthesizers, LNA's, and baseband processing. Low power mixed signal design. Data communications functions including clock recovery. CAD tools for analog design including simulation and synthesis.
Analysis and Design of VLSI Analog-Digital Interface Integrated Circuits: Read More [+]

Rules & Requirements

Prerequisites: Electrical Engineering 140

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Instructor: Boser

Formerly known as: Electrical Engineering 247

Analysis and Design of VLSI Analog-Digital Interface Integrated Circuits: Read Less [-]

Terms offered: Spring 2020, Spring 2019, Spring 2018
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.
Analog Integrated Circuits: Read More [+]

Rules & Requirements

Prerequisites: MAS-IC students only

Credit Restrictions: Students will receive no credit for EE W240A after taking EE 140 or EE 240A.

Hours & Format

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

Summer: 10 weeks - 4.5 hours of web-based lecture and 1.5 hours of web-based discussion per week

Online: This is an online course.

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Instructors: Alon, Sanders, Nguyen

Analog Integrated Circuits: Read Less [-]

Terms offered: Spring 2020, Spring 2019, Fall 2015
Analysis and optimized design of monolithic operational amplifiers and wide-band amplifiers; methods of achieving wide-band amplification, gain-bandwidth considerations; analysis of noise in integrated circuits and low noise design. Precision passive elements, analog switches, amplifiers and comparators, voltage reference in NMOS and CMOS circuits, Serial, successive-approximation, and parallel analog-to-digital converts. Switched-capacitor and CCD filters. Applications to codecs, modems.
Advanced Analog Integrated Circuits: Read More [+]

Rules & Requirements

Prerequisites: EE W240A; MAS-IC students only

Credit Restrictions: Students will receive no credit for EE W240B after taking EE 240B.

Hours & Format

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

Summer: 10 weeks - 4.5 hours of web-based lecture per week

Online: This is an online course.

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Formerly known as: Electrical Engineering W240

Advanced Analog Integrated Circuits: Read Less [-]

Terms offered: Spring 2017, Spring 2016
Architectural and circuit level design and analysis of integrated analog-to-digital and digital-to-analog interfaces in modern CMOS and BiCMOS VLSI technology. Analog-digital converters, digital-analog converters, sample/hold amplifiers, continuous and switched-capacitor filters. Low power mixed signal design techniques. Data communications systems including interface circuity. CAD tools for analog design for simulation and synthesis.
Analysis and Design of VLSI Analog-Digital Interface Integrated Circuits: Read More [+]

Rules & Requirements

Prerequisites: EE W240A; MAS-IC students only

Credit Restrictions: Students will receive no credit for EE W240C after taking EE 240C.

Hours & Format

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

Summer: 10 weeks - 4.5 hours of web-based lecture per week

Online: This is an online course.

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Instructor: Boser

Formerly known as: Electrical Engineering W247

Analysis and Design of VLSI Analog-Digital Interface Integrated Circuits: Read Less [-]

Terms offered: Spring 2020, Spring 2019, Spring 2018
Analysis and design of MOS and bipolar large-scale integrated circuits at the circuit level. Fabrication processes, device characteristics, parasitic effects static and dynamic digital circuits for logic and memory functions. Calculation of speed and power consumption from layout and fabrication parameters. ROM, RAM, EEPROM circuit design. Use of SPICE and other computer aids.
Advanced Digital Integrated Circuits: Read More [+]

Rules & Requirements

Prerequisites: 141

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Instructors: Nikolic, Rabaey

Formerly known as: Electrical Engineering 241

Advanced Digital Integrated Circuits: Read Less [-]

Terms offered: Fall 2015, Fall 2014, Spring 2014
CMOS devices and deep sub-micron manufacturing technology. CMOS inverters and complex gates. Modeling of interconnect wires. Optimization of designs with respect to a number of metrics: cost, reliability, performance, and power dissipation. Sequential circuits, timing considerations, and clocking approaches. Design of large system blocks, including arithmetic, interconnect, memories, and programmable logic arrays. Introduction to design methodologies, including laboratory experience.
Introduction to Digital Integrated Circuits: Read More [+]

Rules & Requirements

Prerequisites: MAS-IC students only

Credit Restrictions: Students will receive no credit for W241A after taking EE 141 or EE 241A.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of web-based lecture and 4 hours of web-based discussion per week

Summer: 10 weeks - 4.5 hours of web-based lecture and 6 hours of web-based discussion per week

Online: This is an online course.

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Instructors: Alon, Rabaey, Nikolic

Introduction to Digital Integrated Circuits: Read Less [-]

Terms offered: Spring 2017, Spring 2016, Spring 2015
Analysis and design of MOS and bipolar large-scale integrated circuits at the circuit level. Fabrication processes, device characteristics, parasitic effects static and dynamic digital circuits for logic and memory functions. Calculation of speed and power consumption from layout and fabrication parameters. ROM, RAM, EEPROM circuit design. Use of SPICE and other computer aids.
Advanced Digital Integrated Circuits: Read More [+]

Rules & Requirements

Prerequisites: EE W241A; MAS-IC students only

Credit Restrictions: Students will receive no credit for EE W241B after taking EE 241B.

Hours & Format

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

Summer: 10 weeks - 4.5 hours of web-based lecture per week

Online: This is an online course.

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Instructors: Nikolic, Rabaey

Formerly known as: Electrical Engineering W241

Advanced Digital 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: 20N and 140 or equivalent

Credit Restrictions: Students will receive no credit for Electrical Engineering 242A after taking Electrical Engineering 142.

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/Graduate

Grading: Letter grade.

Formerly known as: Electrical Engineering 242M

Integrated Circuits for Communications: Read Less [-]

Terms offered: Fall 2020, Fall 2014
Analysis, evaluation and design of present-day integrated circuits for communications application, particularly those for which nonlinear response must be included. MOS, bipolar and BICMOS circuits, audio and video power amplifiers, optimum performance of near-sinusoidal oscillators and frequency-translation circuits. Phase-locked loop ICs, analog multipliers and voltage-controlled oscillators; advanced components for telecommunication circuits. Use of new CAD tools and systems.
Advanced Integrated Circuits for Communications: Read More [+]

Rules & Requirements

Prerequisites: 142, 240

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Instructor: Niknejad

Formerly known as: Electrical Engineering 242

Advanced Integrated Circuits for Communications: Read Less [-]

Terms offered: Spring 2020, Spring 2019, Spring 2018
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: EE W240A; MAS-IC students only

Credit Restrictions: Students will receive no credit for EE W242A after taking EE 142, EE 242A, or EE 242B.

Hours & Format

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

Summer: 10 weeks - 4.5 hours of web-based lecture and 1.5 hours of web-based discussion per week

Online: This is an online course.

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Instructor: Niknejad

Formerly known as: Electrical Engineering W142

Integrated Circuits for Communications: Read Less [-]

Terms offered: Spring 2017, Spring 2016
Analysis, evaluation, and design of present-day integrated circuits for communications application, particularly those for which nonlinear response must be included. MOS, bipolar and BICMOS circuits, audio and video power amplifiers, optimum performance of near-sinusoidal oscillators and frequency-translation circuits. Phase-locked loop ICs, analog multipliers and voltage-controlled oscillators; advanced components for telecommunication circuits. Use of new CAD tools and systems.
Advanced Integrated Circuits for Communications: Read More [+]

Rules & Requirements

Prerequisites: EE W240A, EE W242A; MAS-IC students only

Credit Restrictions: Students will receive no credit for EE W242B after taking EE 242B.

Hours & Format

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

Summer: 10 weeks - 4.5 hours of web-based lecture per week

Online: This is an online course.

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Instructor: Niknejad

Formerly known as: Electrical Engineering W242

Advanced Integrated Circuits for Communications: Read Less [-]

Terms offered: Spring 2014, Spring 2012, Spring 2011
The key processes for the fabrication of integrated circuits. Optical, X-ray, and e-beam lithography, ion implantation, oxidation and diffusion. Thin film deposition. Wet and dry etching and ion milling. Effect of phase and defect equilibria on process control.
Advanced IC Processing and Layout: Read More [+]

Rules & Requirements

Prerequisites: 143 and either 140 or 141

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Advanced IC Processing and Layout: Read Less [-]

Terms offered: Fall 2016, Fall 2015, Fall 2014
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.
Fundamental Algorithms for Systems Modeling, Analysis, and Optimization: Read More [+]

Rules & Requirements

Prerequisites: Graduate standing

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Instructors: Keutzer, Lee, Roychowdhury, Seshia

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

Terms offered: Fall 2015
The modeling, analysis, and optimization of complex systems require a range of algorithms and design tools. This course reviews the fundamental techniques underlying the design methodology for complex systems, using integrated circuit design as an example. Topics include design flows, discrete and continuous models and algorithms, and strategies for implementing algorithms efficiently and correctly in software.
Fundamental Algorithms for System Modeling, Analysis, and Optimization: Read More [+]

Rules & Requirements

Prerequisites: MAS-IC students only

Credit Restrictions: Students will receive no credit for W244 after taking 144 and 244.

Hours & Format

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

Summer: 10 weeks - 4.5 hours of web-based lecture per week

Online: This is an online course.

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Instructors: Keutzer, Lee, Roychowdhury, Seshia

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

Terms offered: Spring 2013, Spring 2012, Spring 2011
Parametric design and optimal design of MEMS. Emphasis on design, not fabrication. Analytic solution of MEMS design problems to determine the dimensions of MEMS structures for specified function. Trade-off of various performance requirements despite conflicting design requirements. Structures include flexure systems, accelerometers, and rate sensors.
Parametric and Optimal Design of MEMS: Read More [+]

Rules & Requirements

Prerequisites: Graduate standing or consent of instructor

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Instructors: Lin, Pisano

Formerly known as: 219

Also listed as: MEC ENG C219

Parametric and Optimal Design of MEMS: 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: Electrical Engineering 40 or 100 or consent of instructor required

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

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/Graduate

Grading: Letter grade.

Instructors: Maharbiz, Nguyen, Pister

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

Terms offered: Spring 2020, Spring 2019, Spring 2018
Physics, fabrication, and design of micro-electromechanical systems (MEMS). Micro and nanofabrication processes, including silicon surface and bulk micromachining and non-silicon micromachining. Integration strategies and assembly processes. Microsensor and microactuator devices: electrostatic, piezoresistive, piezoelectric, thermal, magnetic transduction. Electronic position-sensing circuits and electrical and mechanical noise. CAD for MEMS. Design project is required.
Introduction to MEMS Design: Read More [+]

Rules & Requirements

Prerequisites: Graduate standing in engineering or science; undergraduates with 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/Graduate

Grading: Letter grade.

Instructors: Nguyen, Pister

Formerly known as: Electrical Engineering C245, Mechanical Engineering C218

Also listed as: MEC ENG C218

Introduction to MEMS Design: Read Less [-]

Terms offered: Prior to 2007
Physics, fabrication and design of micro electromechanical systems (MEMS). Micro and nano-fabrication processes, including silicon surface and bulk micromachining and non-silicon micromachining. Integration strategies and assembly processes. Microsensor and microactuator devices: electrostatic, piezoresistive, piezoelectric, thermal, and magnetic transduction. Electronic position-sensing circuits and electrical and mechanical noise. CAD for MEMS. Design project is required.
Introduction to MEMS Design: Read More [+]

Rules & Requirements

Prerequisites: MAS-IC students only

Credit Restrictions: Students will receive no credit for EE W247B after taking EE C247B or Mechanical Engineering C218.

Hours & Format

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

Summer: 10 weeks - 4.5 hours of web-based lecture and 1.5 hours of web-based discussion per week

Online: This is an online course.

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Instructors: Nguyen, Pister

Formerly known as: Electrical Engineering W245

Introduction to MEMS Design: Read Less [-]

Terms offered: Prior to 2007
Numerical modelling and analysis techniques are widely used in scientific and
engineering practice; they are also an excellent vehicle for understanding and
concretizing theory.
This course covers topics important for a proper understanding of nonlinearity
and noise: periodic steady state and envelope ("RF") analyses; oscillatory
systems; nonstationary and phase noise; and homotopy/continuation techniques
for solving "difficult" equation systems. An underlying theme of the course is
relevance to different physical domains, from electronics (e.g.,
analog/RF/mixed-signal circuits, high-speed digital circuits, interconnect,
etc.) to optics, nanotechnology, chemistry, biology and mechanics. Hands-on
coding using the MATLAB-based Berkeley Model
Numerical Modeling and Analysis: Nonlinear Systems and Noise: Read More [+]

Objectives & Outcomes

Course Objectives: Homotopy techniques for robust nonlinear equation solution
Modelling and analysis of oscillatory systems
- harmonic, ring and relaxation oscillators
- oscillator steady state analysis
- perturbation analysis of amplitude-stable oscillators
RF (nonlinear periodic steady state) analysis
- harmonic balance and shooting
- Multi-time PDE and envelope methods
- perturbation analysis of periodic systems (Floquet theory)
RF (nonlinear, nonstationary) noise concepts and their application
- cyclostationary noise analysis
- concepts of phase noise in oscillators
Using MAPP for fast/convenient modelling and analysis

Student Learning Outcomes: Students will develop a facility in the above topics and be able to apply them
widely across science and engineering.

Rules & Requirements

Prerequisites: Consent of Instructor

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Instructor: Roychowdhury

Numerical Modeling and Analysis: Nonlinear Systems and Noise: Read Less [-]

Rules & Requirements

Credit Restrictions: Students will receive no credit for Electrical Engineering/Computer Science C249A after completing Electrical Engineering/Computer Science C149.

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/Graduate

Grading: Letter grade.

Instructors: Lee, Seshia

Formerly known as: Electrical Engineering C249M/Computer Science C249M

Also listed as: COMPSCI C249A

Introduction to Embedded Systems: Read Less [-]

Terms offered: Spring 2020, Spring 2019, Spring 2016, Spring 2015
Principles of embedded system design. Focus on design methodologies and foundations. Platform-based design and communication-based design and their relationship with design time, re-use, and performance. Models of computation and their use in design capture, manipulation, verification, and synthesis. Mapping into architecture and systems platforms. Performance estimation. Scheduling and real-time requirements. Synchronous languages and time-triggered protocols to simplify the design process.
Embedded System Design: Modeling, Analysis, and Synthesis: Read More [+]

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/Graduate

Grading: Letter grade.

Instructor: Sangiovanni-Vincentelli

Formerly known as: Electrical Engineering C249/Civil and Environmental Engineering C289

Also listed as: CIV ENG C289

Embedded System Design: Modeling, Analysis, and Synthesis: 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 [+]

Objectives & Outcomes

Course Objectives: •
understand how 2D impulse response or 2D spatial frequency transfer function (or Modulation Transfer Function) allow one to quantify the spatial resolution of an imaging system.
•
understand 2D sampling requirements to avoid aliasing
•
understand 2D filtered backprojection reconstruction from projections based on the projection-slice theorem of Fourier Transforms
•
understand the concept of image reconstruction as solving a mathematical inverse problem.
•
understand the limitations of poorly conditioned inverse problems and noise amplification
•
understand how diffraction can limit resolution---but not for the imaging systems in this class
•
understand the hardware components of an X-ray imaging scanner
•
•
understand the physics and hardware limits to spatial resolution of an X-ray imaging system
•
understand tradeoffs between depth, contrast, and dose for X-ray sources
•
understand resolution limits for CT scanners
•
understand how to reconstruct a 2D CT image from projection data using the filtered backprojection algorithm
•
understand the hardware and physics of Nuclear Medicine scanners
•
understand how PET and SPECT images are created using filtered backprojection
•
understand resolution limits of nuclear medicine scanners
•
understand MRI hardware components, resolution limits and image reconstruction via a 2D FFT
•
understand how to construct a medical imaging scanner that will achieve a desired spatial resolution specification.

Student Learning Outcomes: •
students will be tested for their understanding of the key concepts above
•
undergraduate students will apply to graduate programs and be admitted
•
students will apply this knowledge to their research at Berkeley, UCSF, the national labs or elsewhere
•
students will be hired by companies that create, sell, operate or consult in biomedical imaging

Rules & Requirements

Prerequisites: El Eng 20N and Engineering 7 or equivalent. Knowledge of Matlab or linear algebra assumed

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/Graduate

Grading: Letter grade.

Instructor: Conolly

Also listed as: BIO ENG C261

Medical Imaging Signals and Systems: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester.
Advanced Topics in Electrical Engineering: Read More [+]

Rules & Requirements

Prerequisites: Consent of instructor

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

Hours & Format

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Advanced Topics in Electrical Engineering: Read Less [-]

Terms offered: Spring 2016, Spring 2015, Fall 2014
The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester.
Advanced Topics in Electrical Engineering: Advanced Topics in Computer-Aided Design: 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 - 1-3 hours of lecture per week

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Advanced Topics in Electrical Engineering: Advanced Topics in Computer-Aided Design: Read Less [-]

Terms offered: Spring 2020, Spring 2019, Fall 2018
The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester.
Advanced Topics in Electrical Engineering: Advanced Topics in Solid State Devices: 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 - 1-3 hours of lecture per week

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Advanced Topics in Electrical Engineering: Advanced Topics in Solid State Devices: Read Less [-]

Terms offered: Spring 2019, Fall 2018, Spring 2018
The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester.
Advanced Topics in Electrical Engineering: Advanced Topics in Circuit Design: 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 - 1-3 hours of lecture per week

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Advanced Topics in Electrical Engineering: Advanced Topics in Circuit Design: Read Less [-]

Terms offered: Fall 2014, Fall 2013, Fall 2004
The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester.
Advanced Topics in Electrical Engineering: Advanced Topics in Semiconductor Technology: 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 - 1-3 hours of lecture per week

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Advanced Topics in Electrical Engineering: Advanced Topics in Semiconductor Technology: Read Less [-]

Terms offered: Spring 2014, Fall 2013, Fall 2012
The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester.
Advanced Topics in Electrical Engineering: Advanced Topics in Photonics: 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 - 1-3 hours of lecture per week

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Advanced Topics in Electrical Engineering: Advanced Topics in Photonics: Read Less [-]

Terms offered: Fall 2017, Fall 2016, Spring 2002
The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester.
Advanced Topics in Electrical Engineering: Advanced Topics in Mems, Microsensors, and Microactuators: 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 - 1-3 hours of lecture per week

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Formerly known as: Engineering 210

Advanced Topics in Electrical Engineering: Advanced Topics in Mems, Microsensors, and Microactuators: Read Less [-]

Terms offered: Fall 2018, Fall 2017, Fall 2015
The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester.
Advanced Topics in Electrical Engineering: Advanced Topics in System Theory: 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 - 1-3 hours of lecture per week

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Advanced Topics in Electrical Engineering: Advanced Topics in System Theory: Read Less [-]

Terms offered: Spring 2019, Fall 2018, Fall 2017
The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester.
Advanced Topics in Electrical Engineering: Advanced Topics in Control: 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 - 1-3 hours of lecture per week

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Advanced Topics in Electrical Engineering: Advanced Topics in Control: Read Less [-]

Terms offered: Spring 2019, Spring 2018, Fall 2017
The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester.
Advanced Topics in Electrical Engineering: Advanced Topics in Bioelectronics: 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 - 1-3 hours of lecture per week

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Advanced Topics in Electrical Engineering: Advanced Topics in Bioelectronics: Read Less [-]

Terms offered: Spring 2017, Spring 2016, Fall 2014
The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester.
Advanced Topics in Electrical Engineering: Advanced Topics in Communication Networks: 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 - 1-3 hours of lecture per week

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Advanced Topics in Electrical Engineering: Advanced Topics in Communication Networks: Read Less [-]

Terms offered: Fall 2018, Fall 2016, Fall 2009
The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester.
Advanced Topics in Electrical Engineering: Advanced Topics in Communications and Information Theory: 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 - 1-3 hours of lecture per week

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Advanced Topics in Electrical Engineering: Advanced Topics in Communications and Information Theory: Read Less [-]

Terms offered: Fall 2018, Fall 2017, Fall 2016
The 290 courses cover current topics of research interest in electrical engineering. The course content may vary from semester to semester.
Advanced Topics in Electrical Engineering: Advanced Topics in Signal Processing: 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 - 1-3 hours of lecture per week

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Advanced Topics in Electrical Engineering: Advanced Topics in Signal Processing: Read Less [-]

Terms offered: Spring 2014, Spring 2013, Fall 2009
Organic materials are seeing increasing application in electronics applications. This course will provide an overview of the properties of the major classes of organic materials with relevance to electronics. Students will study the technology, physics, and chemistry of their use in the three most rapidly growing major applications--energy conversion/generation devices (fuel cells and photovoltaics), organic light-emitting diodes, and organic transistors.
Advanced Topics in Electrical Engineering: Organic Materials in Electronics: Read More [+]

Rules & Requirements

Prerequisites: 130; undergraduate general chemistry

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

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Instructor: Subramanian

Advanced Topics in Electrical Engineering: Organic Materials in Electronics: Read Less [-]

Terms offered: Prior to 2007
Seminar-style course presenting an in-depth perspective on one specific domain of integrated circuit design. Most often, this will address an application space that has become particularly relevant in recent times. Examples are serial links, ultra low-power design, wireless transceiver design, etc.
Advanced Topics in Circuit Design: Read More [+]

Rules & Requirements

Prerequisites: MAS-IC students only

Credit Restrictions: Students will receive no credit for W290C after taking 290C.

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

Hours & Format

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

Summer: 10 weeks - 4.5 hours of web-based lecture per week

Online: This is an online course.

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Advanced Topics in Circuit Design: Read Less [-]

Terms offered: Fall 2017, Spring 2016, Spring 2015, Spring 2014
Distributed systems and PDE models of physical phenomena (propagation of waves, network traffic, water distribution, fluid mechanics, electromagnetism, blood vessels, beams, road pavement, structures, etc.). Fundamental solution methods for PDEs: separation of variables, self-similar solutions, characteristics, numerical methods, spectral methods. Stability analysis. Adjoint-based optimization. Lyapunov stabilization. Differential flatness. Viability control. Hamilton-Jacobi-based control.
Control and Optimization of Distributed Parameters Systems: Read More [+]

Rules & Requirements

Prerequisites: Engineering 77, Mathematics 54 (or equivalent), or consent of instructor

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Also listed as: CIV ENG C291F/MEC ENG C236

Control and Optimization of Distributed Parameters Systems: Read Less [-]

Terms offered: Spring 2020, Spring 2018, Spring 2016
Analysis of hybrid systems formed by the interaction of continuous time dynamics and discrete-event controllers. Discrete-event systems models and language descriptions. Finite-state machines and automata. Model verification and control of hybrid systems. Signal-to-symbol conversion and logic controllers. Adaptive, neural, and fuzzy-control systems. Applications to robotics and Intelligent Vehicle and Highway Systems (IVHS).
Hybrid Systems and Intelligent Control: Read More [+]

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Letter grade.

Formerly known as: 291E

Also listed as: MEC ENG C290S

Hybrid Systems and Intelligent Control: Read Less [-]

Terms offered: Summer 2018 8 Week Session, Fall 2011, Fall 2010
Supervised experience in off-campus companies relevant to specific aspects and applications of electrical engineering. Written report required at the end of the semester.
Field Studies in Electrical Engineering: Read More [+]

Rules & Requirements

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

Hours & Format

Fall and/or spring: 15 weeks - 1-12 hours of independent study per week

Summer: 8 weeks - 1-12 hours of independent study per week

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Offered for satisfactory/unsatisfactory grade only.

Field Studies in Electrical Engineering: Read Less [-]

Terms offered: Spring 2020, Spring 2019, Fall 2018
Advanced study in various subjects through special seminars on topics to be selected each year, informal group studies of special problems, group participation in comprehensive design problems, or group research on complete problems for analysis and experimentation.
Group Studies, Seminars, or Group Research: Read More [+]

Rules & Requirements

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

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: The grading option will be decided by the instructor when the class is offered.

Group Studies, Seminars, or Group Research: Read Less [-]

Terms offered: Spring 2019, Summer 2016 10 Week Session, Summer 2016 8 Week Session
Investigation of problems in electrical engineering.
Individual Research: Read More [+]

Rules & Requirements

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

Hours & Format

Fall and/or spring: 15 weeks - 1-12 hours of independent study per week

Summer:
6 weeks - 2.5-30 hours of independent study per week
8 weeks - 1.5-22.5 hours of independent study per week

Additional Details

Subject/Course Level: Electrical Engineering/Graduate

Grading: Offered for satisfactory/unsatisfactory grade only.

Individual Research: Read Less [-]

Terms offered: Fall 2020, Spring 2020, Fall 2019
Discussion of effective teaching techniques. Use of educational objectives, alternative forms of instruction, and proven techniques to enhance student learning. This course is intended to orient new student instructors to more effectively teach courses offered by the Department of Electrical Engineering and Computer Sciences at UC Berkeley.
Teaching Techniques for Electrical Engineering: Read More [+]

Rules & Requirements

Prerequisites: Teaching assistant or graduate student

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

Hours & Format

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

Additional Details

Subject/Course Level: Electrical Engineering/Professional course for teachers or prospective teachers

Grading: Offered for satisfactory/unsatisfactory grade only.

Teaching Techniques for Electrical Engineering: Read Less [-]

Terms offered: Fall 2016, Fall 2015, Fall 2014
Individual study in consultation with the major field adviser, intended to provide an opportunity for qualified students to prepare themselves for the various examinations required of candidates for the Ph.D. (and other doctoral degrees).
Individual Study for Doctoral Students: Read More [+]

Rules & Requirements

Credit Restrictions: Course does not satisfy unit or residence requirements for doctoral degree.

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: 8 weeks - 6-45 hours of independent study per week

Additional Details

Subject/Course Level: Electrical Engineering/Graduate examination preparation

Grading: Offered for satisfactory/unsatisfactory grade only.

Individual Study for Doctoral Students: 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

387 Soda Hall

Phone: 510-642-1042

Fax: 510-642-5775

Visit Department Website

Vice Chair, Masters’ Degree Programs (MEng and MS)

Vladimir Stojanovic, PhD

513 Cory Hall

Phone: 510-664-4322

vlada@berkeley.edu

Vice Chair of Graduate Study and Prelims

Ron Fearing, PhD

725 Sutardja Dai Hall

Phone: 510-642-9193

ronf@eecs.berkeley.edu

Vice Chair of Graduate Study and Prelims

John Wawrzynek, PhD

631 Soda Hall

Phone: 510-643-9434

johnw@cs.berkeley.edu

Executive Director of EECS Center for Student Affairs

Susanne Kauer

221 Cory Hall

skauer@eecs.berkeley.edu

Department Chair

James Demmel, PhD

389 Soda Hall

Phone: 510-642-7699

demmel@cs.berkeley.edu

CS Graduate Admissions and GSI Recruitment

Diana Smith

205 Cory Hall

Phone: 510-642-6285

diana.smith@eecs.berkeley.edu

Graduate Admissions and EE GSI Recruitment

Pat Hernan

205 Cory Hall

Phone: 510-642-9265

Hernan@eecs.berkeley.edu

Director of Grad Matters, EE Grad Advisor

Shirley Salanio

217 Cory Hall

Phone: 510-643-8347

shirley@eecs.berkeley.edu

Graduate Student Services Advisor

Audrey Sillers

367 Soda Hall

Phone: 510-642-9413

araya@eecs.berkeley.edu

Graduate Student Services Advisor

Michael Sun

205 Cory Hall

Phone: 510-643-8347

msun86@eecs.berkeley.edu

Graduate Student Services Advisor

Heather Levien

253B Cory Hall

Phone: 510-642-9413

heather@eecs.berkeley.edu

Computer Science

About the Program

Master of Science (MS)

Doctor of Philosophy (PhD)

Admissions

Admission to the University

Minimum Requirements for Admission

Applicants Who Already Hold a Graduate Degree

Required Documents for Applications

Where to Apply

Admission to the Program

Doctoral Degree Requirements

Normative Time Requirements

Time to Advancement

Curriculum

Preliminary Exams

Oral Examination

Breadth Courses

Qualifying Examination (QE)

Format A

Format B

Thesis Proposal Defense

Normative Time in Candidacy

Advancement to Candidacy

Dissertation Talk

Required Professional Development

Graduate Student Instructor Teaching Requirement

Master's Degree Requirements

Unit requirements

Curriculum

Plan I

Plan II

Advancement to Candidacy

Capstone/Thesis (Plan I)

Capstone/Master's Project (Plan II)

Courses

Select a subject to view courses

Electrical Engineering and Computer Sciences

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 C200A Principles and Techniques of Data Science 4 Units

COMPSCI C249A Introduction to Embedded Systems 4 Units

COMPSCI 250 VLSI Systems Design 4 Units

COMPSCI 252 Graduate Computer Architecture 4 Units

COMPSCI 260A User Interface Design and Development 4 Units

COMPSCI 260B Human-Computer Interaction Research 3 Units

COMPSCI 261 Security in Computer Systems 3 Units

COMPSCI 261N Internet and Network Security 4 Units

COMPSCI 262A Advanced Topics in Computer Systems 4 Units

COMPSCI 262B Advanced Topics in Computer Systems 3 Units

COMPSCI 263 Design of Programming Languages 3 Units

COMPSCI 264 Implementation of Programming Languages 4 Units

COMPSCI 265 Compiler Optimization and Code Generation 3 Units

COMPSCI C267 Applications of Parallel Computers 3 Units

COMPSCI W267 Applications of Parallel Computers 3 Units

COMPSCI 268 Computer Networks 3 Units

COMPSCI 270 Combinatorial Algorithms and Data Structures 3 Units

COMPSCI 271 Randomness and Computation 3 Units

COMPSCI 273 Foundations of Parallel Computation 3 Units

COMPSCI 274 Computational Geometry 3 Units

COMPSCI 276 Cryptography 3 Units

COMPSCI C280 Computer Vision 3 Units

COMPSCI C281A Statistical Learning Theory 3 Units

COMPSCI C281B Advanced Topics in Learning and Decision Making 3 Units

COMPSCI 282A Designing, Visualizing and Understanding Deep Neural Networks 4 Units

COMPSCI 284A Foundations of Computer Graphics 4 Units

COMPSCI 284B Advanced Computer Graphics Algorithms and Techniques 4 Units

COMPSCI 285 Deep Reinforcement Learning, Decision Making, and Control 3 Units

COMPSCI 286A Introduction to Database Systems 4 Units

COMPSCI 286B Implementation of Data Base Systems 3 Units

COMPSCI 287 Advanced Robotics 3 Units

COMPSCI 287H Algorithmic Human-Robot Interaction 4 Units