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

Prerequisites: MATH 53, MATH 54, COMPSCI 70, and COMPSCI 188; or consent of instructor.

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

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

Class Schedule (Fall 2024):
CS 189/289A – TuTh 14:00-15:29, Haas Faculty Wing F295 – Jennifer Listgarten

Class homepage on inst.eecs

Department Notes: As of Fall 2019, CS289 has a final exam and no project.

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