CS229T/STATS231: Statistical Learning Theory

Announcements
Administrative information
Time/location:
Instructor: Tengyu Ma
Course assistants: Office hours: see Google Calendar
Contact: Please use Piazza for questions and discussions.
Course content
Description: When do machine learning algorithms work and why? How do we formalize what it means for an algorithm to learn from data? How do we use mathematical thinking to design better machine learning methods? This course focuses on developing a theoretical understanding of the statistical properties of learning algorithms.
Topics:
Prerequisites:
Grading
Coursework:

By default, no late work is accepted. Under extentuating circumstances, you may request an extension by contacting the course staff.

Collaboration policy: we encourage you to form study groups and discuss homeworks. However, you must write up all homeworks from scratch independently without referring to any notes from the joint session. Please follow the honor code.
Texts and References

There is no required text for the course. A number of useful references:

Schedule (subject to change)
Week 1
  • Mon 09/24: Lecture 1: overview, formulation of prediction problems, error decomposition
  • Wed 09/26: Lecture 2: asymptotics of maximum likelihood estimators (MLE)
Week 2
  • Mon 10/01: Lecture 3: uniform convergence, overview
  • Mon 10/01: Homework 1 out
  • Wed 10/03: Lecture 4: uniform convergence, finite hypothesis class
  • Wed 10/03: Homework 0 (warmup) due
Week 3
  • Mon 10/08: Lecture 5
  • Wed 10/11: Lecture 6
  • Wed 10/11: Homework 1 due
  • Wed 10/11: Homework 2 out
Week 4
  • Mon 10/15: Lecture 7
  • Wed 10/17: Lecture 8
Week 5
  • Mon 10/22: Lecture 9
  • Wed 10/24: Lecture 10
Week 6
  • Mon 10/29: Lecture 11
  • Wed 10/31: Lecture 12
  • Wed 10/31: Homework 2 (uniform convergence) due
  • Wed 10/31: Homework 3 out
Week 7
  • Mon 11/05: Lecture 13
  • Wed 11/07: Lecture 14
Week 8
  • Mon 11/12: Lecture 15
  • Wed 11/14: Lecture 16
  • Wed 11/14: Exam (6-10pm, location TBD)
Week 9
  • Mon 11/26: Lecture 17
  • Wed 11/28: Lecture 18
  • Wed 11/28: Homework 3 due
Week 10
  • Mon 12/03: Lecture 19
  • Wed 12/05: Lecture 20
  • Sun 12/09: Paper review due
  • Sun 12/09: Final project due (if you didn't do the paper review)