Spring 2024
COMPSCI 289A 999 - DIS 999
Introduction to Machine Learning
Jan 16, 2024 - May 03, 2024
12:00 am
Class #:29853
Units: 4
Instruction Mode:
In-Person Instruction
Time Conflict Enrollment Allowed
Offered through
Electrical Engineering and Computer Sciences
Current Enrollment
Total Open Seats:
0
Enrolled: 16
Waitlisted: 0
Capacity: 16
Waitlist Max: 300
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
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
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