2022 Fall
COMPSCI 289A 001 - LEC 001
Introduction to Machine Learning
Jennifer Listgarten, Jitendra Malik
Class #:27765
Units: 4
Instruction Mode:
In-Person Instruction
Offered through
Electrical Engineering and Computer Sciences
Current Enrollment
Total Open Seats:
15
Enrolled: 32
Waitlisted: 0
Capacity: 47
Waitlist Max: 100
Open Reserved Seats:
39 reserved for Electrical Engineering and Computer Sciences - Master of Engineering Students
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 13TH
08:00 am - 11:00 am
Pimentel 1
Evans 55
Evans 70
Evans 71
Evans 75
Other classes by Jennifer Listgarten
Other classes by Jitendra Malik
Course Catalog Description
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.
Rules & Requirements
Requisites
- Graduate students NOT in the Master of Engineering Program other those in EECS
Credit Restrictions
Students will receive no credit for Comp Sci 289A after taking Comp Sci 189.
Repeat Rules
Course is not repeatable for credit.
Reserved Seats
Current Enrollment
Open Reserved Seats:
39 reserved for Electrical Engineering and Computer Sciences - Master of Engineering Students
Textbooks & Materials
See class syllabus or https://calstudentstore.berkeley.edu/textbooks for the most current information.
Guide to Open, Free, & Affordable Course Materials