CS 289A. Introduction to Machine Learning
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.
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: letter
Final exam status: Written final exam conducted during the scheduled final exam period
Class Schedule (Spring 2024):
CS 189/289A – MoWe 18:30-19:59, Wheeler 150 –
Jonathan Shewchuk
Class Schedule (Fall 2024):
CS 189/289A – TuTh 14:00-15:29, Haas Faculty Wing F295 –
Jennifer Listgarten
Department Notes: As of Fall 2019, CS289 has a final exam and no project.
Related Areas: