CSE 142Machine Learning
Introduction to machine learning algorithms and their applications. Topics include classification learning, density estimation and Bayesian learning regression, and online learning. Provides introduction to standard learning methods such as neural networks, decision trees, boosting, and nearest neighbor techniques.
Prerequisite(s): CSE 40 or STAT 132; and CSE 101 or CSE 101P; and AM 30, or MATH 22, or MATH 23A; and STAT 131 or CSE 107. Enrollment restricted to computer science and biomolecular engineering and bioinformatics majors during First Pass enrollment.
- Enroll in lecture and associated discussion section.
- Enroll in lecture and associated discussion section. Enrollment restricted to computer science and biomolecular engineering and bioinformatics majors during First Pass enrollment. Major restrictions lifted after First Pass enrollment.
Find a section and add it to your scheduleLive seat counts, time-conflict checks and the walk from your previous class.
When it runs
Fall 2026Open Fall 2026
| Section | Type | When | Where | Instructor | Seats |
|---|---|---|---|---|---|
| 01 | Lecture | Mon Wed Fri 12:00pm–1:05pm | Steven Acad 150 | A. J. Rudnick | Wait List 120/120 |
Spring 2026Open Spring 2026
| Section | Type | When | Where | Instructor | Seats |
|---|---|---|---|---|---|
| 01 | Lecture | Tue Thu 1:30pm–3:05pm | J Baskin Engr 152 | C. Liu | Open 108/115 |
Fall 2025Open Fall 2025
| Section | Type | When | Where | Instructor | Seats |
|---|---|---|---|---|---|
| 01 | Lecture | Mon Wed Fri 12:00pm–1:05pm | ClassroomUnit 002 | A. J. Rudnick | Open 124/127 |
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