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 | Supplementary notes | 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
Week 9: Mar 11 - Mar 16 Ridgeless
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
Week 13: Apr 8 - Apr 12 UQ under distribution shift Hw 3 due Fri Apr 12
Week 14: Apr 15 - Apr 19 Scoring and calibration
Week 15: Apr 22 - Apr 26 Buffer/spillover
Week 16 Apr 29 - May 3 Class presentations Project due Fri May 3


Homework


Project


Supplementary notes

Here are some supplementary notes, on some topics adjacent to those from lectures.

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.