| layout | no-sidebar |
|---|---|
| title | Home |
| nav_exclude | true |
Machine learning is at the core of modern artificial intelligence, transforming how we approach problems in vision, language, robotics, recommendation systems, and countless other areas. EECS 189/289A introduces the theoretical foundations, algorithms, and applications of machine learning, combining mathematical rigor with practical experience. The course explores the full machine-learning pipeline, from problem formulation and working with data to designing and optimizing models. Topics include probability and optimization, clustering and latent-variable models, dimensionality reduction, regression and classification, neural networks, and modern deep-learning architectures.
- Fall 2026
- Spring 2026
- Fall 2025 {%- comment %}
- Fall 2024
- Spring 2024
- Fall 2023
- Spring 2023
- Fall 2022
- Spring 2022
- Fall 2021
- Spring 2021
- Fall 2020
- Spring 2020 {% endcomment %}
- Fall 2019 {%- comment %}
- Spring 2019
- Fall 2018 {% endcomment %}
- Spring 2018
- Fall 2017
-
Provide a rigorous foundation in the mathematics, algorithms, and concepts of machine learning.
-
Prepare students for advanced coursework and research in artificial intelligence, deep learning, computer vision, and natural language processing.
-
Enable students to implement machine-learning algorithms and apply them to real-world problems.
CS 189/289A assumes strong preparation in mathematics and programming. The required prerequisites are:
-
Multivariable calculus: MATH 53.
-
Linear algebra: MATH 54 or equivalent.
-
Probability and discrete mathematics: COMPSCI 70 or equivalent.
You should be comfortable with vector calculus (including gradients and the multivariate chain rule), matrix operations, probability theory (including conditional probability and Bayes’ rule), and writing/debugging complex programs in Python. If you lack preparation in these areas, you are likely to struggle.