Spring 2023
COMPSCI 289A 121 - DIS 121
Introduction to Machine Learning
Class #:29128
Units: 4
Instruction Mode:
In-Person Instruction
Offered through
Electrical Engineering and Computer Sciences
Current Enrollment
Total Open Seats:
0
Enrolled: 0
Waitlisted: 0
Capacity: 0
Waitlist Max: 0
No Reserved Seats
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.
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
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
No Reserved Seats
Textbooks & Materials
See class syllabus or https://calstudentstore.berkeley.edu/textbooks for the most current information.
Guide to Open, Free, & Affordable Course Materials