Spring 2025
COMPSCI 289A 001 - LEC 001
Introduction to Machine Learning
Jonathan Shewchuk
Class #:29568
Units: 4
Instruction Mode:
In-Person Instruction
Time Conflict Enrollment Allowed
Offered through
Electrical Engineering and Computer Sciences
Current Enrollment
Total Open Seats:
3
Enrolled: 22
Waitlisted: 0
Capacity: 25
Waitlist Max: 300
Open Reserved Seats:
6 reserved for Computer Science and Electrical Engineering and Computer Sciences Graduate Students
1 reserved for Master of Design 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
FRI, MAY 16TH
03:00 pm - 06:00 pm
Pimentel 1
Valley Life Sciences 2050
Stanley 105
Evans 10
Other classes by Jonathan Shewchuk
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.
Class Notes
* Time conflicts ARE allowed but no alternate final exam offered.
* Lecture WILL be recorded for playback later.
* Lecture WILL be recorded for playback later.
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
Reserved Seating For This Term
Current Enrollment
Open Reserved Seats:
6 reserved for Computer Science and Electrical Engineering and Computer Sciences Graduate Students
1 reserved for Master of Design Students
Textbooks & Materials
See class syllabus or https://calstudentstore.berkeley.edu/textbooks for the most current information.
Guide to Open, Free, & Affordable Course Materials