Theoretical Statistics and Machine Learning

SDS 391P.6, Spring 2026

Instructor: Pratik Patil (pratikpatil at utexas dot edu)

Lectures: Tuesdays, Thursdays, 2-3.30pm, ECJ 1.306

Office hours: Tuesdays, Thursdays, 3.30-5pm, WEL 5.216H

Handy links: Website, Syllabus, Canvas


Go to:   Schedule | Homework | Resources

Schedule

Here is the estimated class schedule. It is subject to change, depending on time and class interests.

Week 01: Jan 12 - Jan 18 Refreshers: linear algebra and probability note 01, note 02
Week 02: Jan 19 - Jan 25 Variance bounds and Poincare inequalities note 03, note 04
Week 03: Jan 26 - Feb 01 Exponential concentration I:
Laplace transform method and applications
note 05, note 06
Week 04: Feb 02 - Feb 08 Exponential concentration II:
Entropy method and log-Sobolev inequalities
note 07, note 08 Hw 01 due Mon Feb 02
Week 05: Feb 09 - Feb 15 Exponential concentration III:
Isoperimetric and transportation perspectives
note 09, note 10
Week 06: Feb 16 - Feb 22 Metric entropy and covariance estimation note 11, note 12 Hw 02 due Mon Feb 16
Week 07: Feb 23 - Mar 01 Universality and fundamental spectral laws note 13, note 14
Week 08: Mar 02 - Mar 08 Matrix concentration and applications note 15, note 16 Hw 03 due Mon Mar 02
Week 09: Mar 09 - Mar 15 Mid exam review review note Mid exam due
Week 10: Mar 16 - Mar 22 (Spring break, no class)
Week 11: Mar 23 - Mar 29 Suprema via the chaining method note 17, note 18
Week 12: Mar 30 - Apr 05 Empirical process theory note 19, note 20
Week 13: Apr 06 - Apr 12 Statistical learning theory note 21, note 22 Hw 04 due Mon Apr 06
Week 14: Apr 13 - Apr 19 Non-parametric regression note 23, note 24
Week 15: Apr 20 - Apr 26 Minimax lower bounds note 25, note 26 Hw 05 due Mon Apr 20
Week 16: Apr 27 - May 03 (Final exam week, no class) Final exam due


Homeworks


Exams


Resources

The course is designed to be self-contained and will often provide references for further details on various topics covered. The following references are wonderful resources for the material covered in this course: