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: 4.0

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

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

Fall: 3 hours of lecture and 1 hour of discussion per week
Spring: 3 hours of lecture and 1 hour of discussion per week

Grading basis: letter

Final exam status: No final exam

Class Schedule (Spring 2020):
MoWe 6:30PM - 7:59PM, Wheeler 150 – Jonathan Shewchuk

Spring 2019 class homepage on bCourses

Class homepage on inst.eecs

General Catalog listing

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

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