CS 189/289A: Intro to Machine Learning
UC Berkeley, Spring 2026
Wheeler 150, Tuesdays and Thursdays 2pm-3:30pm
Course staff email: cs189-instructors@berkeley.edu. This email is monitored by the instructors, the head TAs, and a few lead TAs.
Welcome to Week 6 of CS 189/289A!
If you are a waitlisted or enrolled student and need access to Ed/Gradescope, please fill out this form. Students auditing the course will unfortunately not be added to Ed/Gradescope, but all course materials will be posted on this site.
Schedule
| Wk. | Date | Lecture | Recommended Reading | Sections | HW |
|---|---|---|---|---|---|
| 1 |
Tue Jan 20 | 1. Introduction + ML problem framing |
No Section | ||
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Thu Jan 22 | 2. Data Tools PDF / Video |
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| 2 |
Tue Jan 27 | 3. Machine Learning Mechanics - Terminology and Techniques PDF / Video |
1. Discussion |
Homework 1
(due Fri Feb 20) Part 1: Written / Part 1: Coding / Part 2: Coding |
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Thu Jan 29 | 4. Clustering, Probability Review PDF / Video |
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| 3 |
Tue Feb 03 | 5. Intro to Maximum Likelihood Estimation, Multivariate Gaussians, Mixture of Gaussians PDF / Video |
2.3.2 (Likelihood function), 3.1.3 (Multinomial distribution), 3.2 (Multivariate Gaussian), 3.2.1 (Geometry of Gaussian), 3.2.7 (Maximum likelihood), Appendix C (Lagrange multipliers), Optional: 2.2.2 (Expectations and covariances), 3.2.9 (Mixtures of Gaussians) | 2. Discussion |
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Thu Feb 05 | 6. Multivariate Gaussians & Mixture of Gaussians PDF / Video |
2.2.2 (Expectations and covariances), 3.1.3 (Multinomial distribution), 3.2 (Multivariate Gaussian), 3.2.1 (Geometry of Gaussian), 3.2.9 (Mixtures of Gaussians), 15.2 (Mixtures of Gaussians), 15.3 (Mixture model log-likelihood), Appendix C (Lagrange multipliers) | |||
| 4 |
Tue Feb 10 | 7. Mixture of Gaussians & Linear Regression PDF / Video |
1.2 (A Tutorial Example), 2.3.4 (Linear regression), 3.2.9 (Mixtures of Gaussians), 4.1.1 (Basis functions), 4.1.2 (Likelihood function), 4.1.3 (Maximum likelihood), 4.1.4 (Geometry of least squares), 15.2 (Mixtures of Gaussians), 15.2.1 (Likelihood function), Appendix A.3 (Matrix Derivatives) | 3. Discussion |
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Thu Feb 12 | 8. Linear Regression PDF / Video |
1.2.2 (Linear models), 1.2.3 (Error function), 1.2.5 (Regularization), 1.2.6 (Model selection), 2.1.3 (Bayes’ theorem), 2.3.4 (Linear regression), 2.6.2 (Regularization), 4.1.1 (Basis functions), 4.1.2 (Likelihood function), 4.1.3 (Maximum likelihood), 4.1.4 (Geometry of least squares), 4.1.6 (Regularized least squares), 9.2 (Weight Decay), 9.2.2 (Generalized weight decay), Appendix A.3 (Matrix Derivatives) | |||
| 5 |
Tue Feb 17 | 9. Linear Regression & Regularization PDF / Video |
4. Discussion |
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Thu Feb 19 | 10. Regularization, Classification PDF / Video |
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| 6 |
Tue Feb 24 | 11. Classification |
5. Discussion |
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Thu Feb 26 |