Advanced Topics in Statistical Learning: Spring 2024

Stat 241B / CS 281B

Instructor: 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)
Go to:   Schedule | Homework | Project | Other resources

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 Fri May 3

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.