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Advanced Topics in Statistical Learning: Spring 2024
Stat 241B / CS 281BInstructor: Ryan Tibshirani (ryantibs at berkeley dot edu)GSI: Seunghoon Paik (shpaik at berkeley dot edu) Class times: Mon, Weds, Fri, 2-3pm, Tan 180 Office hours: RT: Wednesdays, 3-4pm, Evans 417 SP: Thursdays 3:30-5:30pm, Evans 444 Handy links: • Syllabus • GitHub repo (source files for lectures and homeworks) • Ed discussion (for class discussions and announcements) • bCourses (for grade-keeping and homework solutions) |
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Schedule
Here is the estimated class schedule. It is subject to change, depending on time and class interests.| Week 1: Jan 17 - Jan 19 | Stat/ML in a nutshell (review) | pdf, source | |
| Week 2: Jan 22 - Jan 26 | Nearest neighbors and kernels | pdf, source | |
| Week 3: Jan 29 - Feb 2 | Splines and RKHS methods | pdf, source | |
| Week 4: Feb 5 - Feb 9 | Minimax theory | pdf, source | Hw 1 due Fri Feb 9 |
| Week 5: Feb 12 - Feb 16 | Empirical process theory | pdf, source | |
| Week 6: Feb 21 - Feb 23 | Buffer/spillover | ||
| Week 7: Feb 26 - Mar 1 | Lasso | pdf, source | Hw 2 due Fri Mar 1 |
| Week 8: Mar 4 - Mar 8 | Ridge | pdf, source | |
| Week 9: Mar 11 - Mar 16 | Ridgeless | pdf, source | |
| Week 10: Mar 18 - Mar 22 | Buffer/spillover | Hw 3 due Fri Mar 22 | |
| Week 11: Mar 25 - Mar 29 | (Spring break, no class) | ||
| Week 12: Apr 1 - Apr 5 | Conformal prediction | pdf, source | |
| Week 13: Apr 8 - Apr 12 | Conformal under distribution shift | pdf, source | Hw 3 due Fri Apr 12 |
| Week 14: Apr 15 - Apr 19 | Calibration, scoring, and Blackwell | outline, last year's notes | |
| Week 15: Apr 22 - Apr 26 | Buffer/spillover | ||
| Week 16 Apr 29 - May 3 | Class presentations | Project due Mon May 6 |
Homework
Project
See here for instructions and timeline.Other resources
There is no course textbook. The lecture notes will be mostly self-contained, but will often provide references for further details on the topics they cover. Below are some general excellent references that may be helpful as well.- Devroye, Gyrofi, Lugosi, A Probabilistic Theory of Pattern Recognition, 1996.
- Gyrofi, Kohler, Krzyzak, Harro Walk, A Distribution-Free Theory of Nonparametric Regression, 2002.
- Wasserman, All of Statistics, 2004.
- Wasserman, All of Nonparametric Statistics, 2006.
- Hastie, Tibshirani, Friedman, The Elements of Statistical Learning, 2009.
- Hastie, Tibshirani, Wainwright, Statistical Learning with Sparsity, 2015.
- Mohri, Rostamizadeh, Talwalkar, Foundations of Machine Learning, 2018.
- Wainwright, High-Dimensional Statistics: A Non-Asymptotic Viewpoint, 2019.
- Hardt, Recht, Patterns, Predictions, and Actions, 2022.