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

Related Areas:

• Artificial Intelligence (AI)

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

Credit Restrictions: Students will receive no credit for Comp Sci 189 after taking Comp Sci 289A.

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: letter

Final Exam Status: Written final exam conducted during the scheduled final exam period

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Class Schedule (Fall 2025):
CS 189/289A – TuTh 14:00-15:29, Valley Life Sciences 2050 – Joseph E. Gonzalez, Narges Norouzi

Class Notes
* Time conflicts are NOT allowed.

* Lecture WILL be recorded for playback later.

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