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.

Units: 4

Also Offered As: COMPSCI 289A

Related Areas:

• Artificial Intelligence (AI)

Prerequisites: Mathematics 53, 54; Computer Science 70; Computer Science 188 or consent of instructor.

Formats:
Fall: 3.0 hours of lecture and 1.0 hours of discussion per week
Spring: 3.0 hours of lecture and 1.0 hours of discussion per week

Grading Basis: Student Option

Final Exam Status: Yes

---

Class Schedule (Spring 2026):
CS 189/289A – TuTh 14:00-15:29, Wheeler 150 – Alex Dimakis, Jennifer Listgarten

Class Notes
- Lectures will be recorded.

- Seats reserved for students with enrollment permission are not open. They are reserved for students in internal programs. Please DO NOT ask faculty or staff for one of these seats. The students who qualify have already been notified.

Links:

• EECS Course Archives (CalNet authentication required)
• CS enrollment policies
• EE enrollment policies
• HKN course maps