Catalog Description: Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications.

Units: 4

Also Offered As: COMPSCI 189

Related Areas:

• Artificial Intelligence (AI)

Prerequisites: MATH 53 and MATH 54; and COMPSCI 70 or consent of instructor.

Formats:
Summer: 6.0 hours of lecture and 2.0 hours of discussion per week
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.

Class Schedule (Fall 2026):
CS 189/289A – TuTh 17:00-18:29, Dwinelle 155 – Joseph E. Gonzalez, Narges Norouzi

Links:

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