2021 Fall
EECS 208 001 - LEC 001
Computational Principles for High-dimensional Data Analysis
Yi Ma, Jiantao Jiao
Class #:32700
Units: 4
Instruction Mode:
In-Person Instruction
Offered through
Electrical Engineering and Computer Sciences
Current Enrollment
Total Open Seats:
15
Enrolled: 45
Waitlisted: 0
Capacity: 60
Waitlist Max: 12
Open Reserved Seats:
23 reserved for Students not in the Master of Engineering Program
Hours & Workload
3 hours of instructor presentation of course materials per week, 8 hours of outside work hours per week, and 1 hours of the exchange of opinions or questions on course material per week.
Final Exam
TUE, DECEMBER 14TH
08:00 am - 11:00 am
Cory 540AB
Other classes by Yi Ma
Other classes by Jiantao Jiao
Course Catalog Description
Introduction to fundamental geometric and statistical concepts and principles of low-dimensional models for high-dimensional signal and data analysis, spanning basic theory, efficient algorithms, and diverse real-world applications. Systematic study of both sampling complexity and computational complexity for sparse, low-rank, and low-dimensional models – including important cases such as matrix completion, robust principal component analysis, dictionary learning, and deep networks.
Class Notes
Rules & Requirements
Repeat Rules
Course is not repeatable for credit.
Reserved Seats
Current Enrollment
Open Reserved Seats:
23 reserved for Students not in the Master of Engineering Program
Textbooks & Materials
See class syllabus or https://calstudentstore.berkeley.edu/textbooks for the most current information.
Guide to Open, Free, & Affordable Course Materials