Statistics 154/254: Statistical Machine Learning
UC Berkeley, Spring 2026
Instructors
Instructor: Hugo Chardon
Office: 313 Evans Hall
Office hours: Wednesdays 2-4pm in Evans 313
hchardon@berkeley.edu
GSI: Lucas Da Rocha Schwengber
Office: 447 Evans Hall
Office hours: Mon 9-11am, Fri 9-11am in Evans 447
ldrs@berkeley.edu
GSI: Josh Davis
Office hours: Tue 3:30-5:30pm at Evans 426, Fri 3-5pm at Evans 428
joshdavis@berkeley.edu
According to the official schedule, the final exam will take place on Tuesday, May 12 from 7pm to 10pm, room TBD.
This website will contain lecture materials and assignments. Day-to-day announcements and discussion will be found in ED. (See links above.)
Materials
The course will be based largely on a subset of the following texts:
- Patterns, predictions, and actions: A story about machine learning Hardt, Recht
- ESL: The Elements of Statistical Learning Hastie, Tibshirani, Friedman
- ISL: An Introduction to Statistical Learning James, Witten, Hastie, Tibshirani
- LTFP: Learning Theory from First Principles F. Bach
Leture notes and additional reading will supplement these texts as necessary.
Some other good sources of reading material are:
- Understanding Machine Learning: From Theory to Algorithms Shalev-Shwartz, Ben-David
- Probabilistic Machine Learning: An Introduction Murphy
Schedule
Lectures will be held Jan 21st, 2026 – May 1st, 2026 from 11am – 12pm in Physics 4.
Labs will be held on Mondays: - Josh: Sec 101 from 1:00pm to 3:00pm and Sec 102 from 3:00pm to 5:00pm in Evans 334. - Lucas: Sec 103 from 5:00pm to 7:00pm in Evans 334 and Sec 104 from 12pm to 2pm in Evans 340.
Here is a link to the official course catalog entry.
The tentative week-by-week course calendar is as follows. The following schedule is aspirational and subject to change as we go.
| Date | Day | Note | Topic | Assignment |
|---|---|---|---|---|
| Jan 21 | Wednesday | Lecture | Course policies | |
| Jan 23 | Friday | Lecture | Unit 1 | |
| Jan 26 | Monday | Lecture + Labs | Linear regression setup | Lab 1 due Tu. 01/27 |
| Jan 28 | Wednesday | Lecture | LR (OLS as ERM and lin. proj.) | |
| Jan 30 | Friday | Lecture | LR (Bayes for least squares, transformations) | |
| Feb 2 | Monday | Lecture + Labs |