录取要求

A bachelor's degree or recognized equivalent from an accredited institution. If you are in your final year of studies and you expect to earn your degree by mid-August of the following year, you may apply. If you are admitted, you will be required to provide proof at that time that you have earned your bachelor's degree, usually in the form of a final official transcript.

If you attended an American university or a university that uses a similar 4.0 scale, a satisfactory scholastic average with a minimum grade-point average (GPA) of 3.0 (B) is required. If you attended a university that does not use the 4.0 system, please do not try and convert your grades to our system - leave that section blank on your application.

Three (3) Letters of Recommendation uploaded as PDFs through the link provided in the online application. Your letters could include details about your goals, research accomplishments, technical and leadership skills, academic work, etc. We suggest to give your recommenders at least a few weeks to write your letters.

The General Test of the Graduate Record Examination (GRE). We do not have minimum or cutoff GRE test scores. Please see the Admission Requirements page for more information.

If you received or are a candidate to receive a degree from an institution outside the United States, please see Minimum Degree Requirements for International Applicants and Evidence of English Language Proficiency.

申请材料清单

Prepare your required materials

GRE Scores - All three sections of the GRE are required. Take the exam by November. GRE scores will remain valid for up to five years. Send your scores electronically to Institution Code 4833.

Proof of English Proficiency -If your previous degree is from a university in a non-English speaking country, then you need to take the TOEFL or IELTS exam by November. TOEFL scores will remain valid for up to two years. Send your scores electronically to Institution Code 4833.

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?

3 Letters of Recommendation - Your letters could include details about your goals, research accomplishments, technical and leadership skills, academic work, etc. We suggest to give your recommenders at least a few weeks to write your letters and upload them as PDFs through the link (provided from your online application).

Unofficial Transcripts - Please do not mail transcripts. Simply upload unofficial transcripts as PDFs through the online application. Transcripts not in English or Spanish must be translated prior to uploading to the application.

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 application.

Start your application 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 by December 15th.

Enter your exam scores into the application. It is fine for us to receive your official scores electronically after the deadline as long as we have your scores in the application.

Pay the application fee (waivers available).

Submit your online application before the deadline of December 15, 2017, 8:59pm Pacific time.

专业介绍

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

Master of Engineering (MEng)

The Master of Engineering (MEng) in Electrical Engineering & Computer Sciences, first offered by the EECS Department in the 2011-2012 academic year, is a professional master’s with a larger tuition than our other programs and is for students who plan to join the engineering profession immediately following graduation. The accelerated program is designed to develop professional engineering leaders who understand the technical, economic, and social issues of technology. This interdisciplinary experience spans one academic year and includes three major components: (1) An area of technical concentration; (2) Courses in leadership skills; (3) A rigorous capstone project experience.

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.

课程设置

• EE
• EE 16AD esigning Information Devices and Systems I
• EE 16BDesigning Information Devices and Systems II
• EE 49Electronics for the Internet of Things
• EE 105Microelectronic Devices and Circuits
• EECS C106BRobotic Manipulation and Interaction
• EE 113Power Electronics
• EE 117Electromagnetic Fields and Waves
• EE 120Signals and Systems
• EE 122Introduction to Communication Networks
• EE 123Digital Signal Processing
• EECS 126Probability and Random Processes
• EECS 127Optimization Models in Engineering
• EE 130Integrated-Circuit Devices
• EE 134Fundamentals of Photovoltaic Devices
• EE 137BIntroduction to Electric Power Systems
• EE 140Analog Integrated Circuits
• EE 143Microfabrication Technology
• EECS 151Introduction to Digital Design and Integrated Circuits
• EECS 151LAIntroduction to Digital Design and Integrated Circuits Lab
• EECS 151LBIntroduction to Digital Design and Integrated Circuits Lab
• EECS 151LBIntroduction to Digital Design and Integrated Circuits Lab
• EE 192Mechatronic Design Laboratory
• EE 194Special Topics
• EE 197Field Study
• EE 197Field Study
• EE 198Directed Group Study for Advanced Undergraduates
• EE 198EECS Pie Robotics
• EE 198EECS Pie Robotics
• EECS 206BRobotic Manipulation and Interaction
• EE 213APower Electronics
• EECS 219CFormal Methods: Specification, Verification, and Synthesis
• EE C220CExperiential Advanced Control Design II
• EE C222Nonlinear Systems
• EE 223Stochastic Systems: Estimation and Control
• EE 225BDigital Image Processing
• EECS 227ATOptimization Models in Engineering
• EE C227CConvex Optimization and Approximation
• EE 230AIntegrated-Circuit Devices
• EE 230BSolid State Devices
• EE 232Lightwave Devices
• EE 240AAnalog Integrated Circuits
• EE 240BAdvanced Analog Integrated Circuits
• EE 241BAdvanced Digital Integrated Circuits
• EE C247BIntroduction to MEMS Design
• EECS 251AIntroduction to Digital Design and Integrated Circuits
• EECS 251LAIntroduction to Digital Design and Integrated Circuits Lab
• EECS 251LBIntroduction to Digital Design and Integrated Circuits Lab
• EECS 251LBIntroduction to Digital Design and Integrated Circuits Lab
• EE 290CAdvanced Topics in Electrical Engineering: Advanced Topics in Circuit Design
• EE 290PAdvanced Topics in Electrical Engineering: Advanced Topics in Bioelectronics
• EE C291EHybrid Systems and Intelligent Control
• EE 298Group Studies, Seminars, or Group Research
• EE 298Group Studies, Seminars, or Group Research
• EE 375Teaching Techniques for Electrical Engineering
• CS
• CS C8Foundations of Data Science
• CS 9ASelf-paced courses
• CS 9CSelf-paced courses
• CS 9ESelf-paced courses
• CS 9FSelf-paced courses
• CS 9GSelf-paced courses
• CS 9HSelf-paced courses
• CS 10The Beauty and Joy of Computing
• CS 24Berkeley Through the Lens
• CS 36CS Scholars Seminar: The Educational Climate in CS & CS61A technical discussions
• CS 47ASelf-paced courses
• CS 47BSelf-paced courses
• CS 47CSelf-paced courses
• CS 61AThe Structure and Interpretation of Computer Programs
• CS 61BData Structures
• CS 61CMachine Structures
• CS 70Discrete Mathematics and Probability Theory
• CS 88Computational Structures in Data Science
• CS C100Principles & Techniques of Data Science
• EECS C106BRobotic Manipulation and Interaction
• EECS 126Probability and Random Processes
• EECS 127Optimization Models in Engineering
• EECS 151Introduction to Digital Design and Integrated Circuits
• EECS 151LAIntroduction to Digital Design and Integrated Circuits Lab
• EECS 151LBIntroduction to Digital Design and Integrated Circuits Lab
• EECS 151LBIntroduction to Digital Design and Integrated Circuits Lab
• CS 152Computer Architecture and Engineering
• CS 160User Interface Design and Development
• CS 161Computer Security
• CS 162Operating Systems and System Programming
• CS 164Programming Languages and Compilers
• CS 170Efficient Algorithms and Intractable Problems
• CS 174Combinatorics and Discrete Probability
• CS 184Foundations of Computer Graphics
• CS 186Introduction to Database Systems
• CS 188Introduction to Artificial Intelligence
• CS 189Introduction to Machine Learning
• CS 194Computational Design and Fabrication
• CS 194Designing, Visualizing and Understanding Deep Neural Networks
• CS 195Social Implications of Computer Technology
• CS 198iOS DeCal
• CS 198GamesCrafters
• CS 198UCBUGG: 3D Modeling and Animation DeCal
• CS 198Ruby on Rails DeCal
• CS 198Web Design DeCal
• CS 198CS Scholars Group Study (61B)
• CS 198CS Scholars Group Study (70)
• CS 198Smash Bros DeCal: The Anatomy of a Video Game Success
• CS 198Going Down the EECS Stack DeCal
• CS 198DeCal: How to Build the Future
• CS 198Game Design and Development DeCal
• CS 198Blockchain for Developers DeCal
• CS 198Blockchain Fundamentals DeCal
• CS 198The Poetry of Computer Science, The Computer Science of Poetry: Philosophy of Computation DeCal
• CS 198Virtual Reality DeCal
• CS 198Machine Learning DeCal
• CS 198Settlers of Catan DeCal
• CS 198Titans of Cybersecurity DeCal
• CS 198UCBUGG: 3D Modeling and Animation
• EECS 206BRobotic Manipulation and Interaction
• EECS 219CFormal Methods: Specification, Verification, and Synthesis
• EECS 227ATOptimization Models in Engineering
• EECS 251AIntroduction to Digital Design and Integrated Circuits
• EECS 251LAIntroduction to Digital Design and Integrated Circuits Lab
• EECS 251LBIntroduction to Digital Design and Integrated Circuits Lab
• EECS 251LBIntroduction to Digital Design and Integrated Circuits Lab
• CS 252Graduate Computer Architecture
• CS 260AUser Interface Design and Development
• CS 262AAdvanced Topics in Computer Systems
• CS C267Applications of Parallel Computers
• CS 271Randomness and Computation
• CS C280Computer Vision
• CS 284AFoundations of Computer Graphics
• CS 286AIntroduction to Database Systems
• CS 289AIntroduction to Machine Learning
• CS 294Visual Object and Activity Recognition
• CS 294Computational Design and Fabrication
• CS 294Designing, Visualizing and Understanding Deep Neural Networks
• CS 294Special Topics in Deep Learning
• CS 294Advanced Cryptography
• CS 294Blockchain, CryptoEconomics, and the Future Directions of Technology, Business, and Law
• CS 294Approximation Algorithms
• CS 294Computational and Theoretical Immunology
• CS 294Seminars on Applications of Deep Learning in Software Engineering and Programming Languages
• CS 298Group Studies Seminars, or Group Research
• CS 298Group Studies Seminars, or Group Research
• CS 298Group Studies Seminars, or Group Research
• CS 298Database Seminar
• CS 370Introduction to Teaching Computer Science
• CS 370Introduction to Teaching Computer Science
• CS 375Teaching Techniques for Computer Science