Statistical Machine Learning

10-702/36-702, Spring 2012

Larry WassermanBaker Hall 228a

Class Assistant: Sharon Cavlovich
Teaching Assistants: Martin Azizyan , Sivaraman Balakrishnan


Date and Time: Tuesday and Thursday, 12:00 - 1:20 pm
Location: SH 125

TA Office hours: Sivaraman: Wednesdays 3-4 (common area outside GHC 8014)
Martin: Thursdays 3-4 (common area outside GHC 8015)

Professor Wasserman's Office hour: Tuesdays 1:30-2:30 Baker Hall 228a

Home Lecture Schedule

Statistical Machine Learning is a second graduate level course in advanced machine learning , assuming students have taken Machine Learning (10-701) and Intermediate Statistics (36-705). The term "statistical" in the title reflects the emphasis on statistical analysis and methodology, which is the predominant approach in modern machine learning.

The course combines methodology with theoretical foundations and computational aspects. It treats both the "art" of designing good learning algorithms and the "science" of analyzing an algorithm's statistical properties and performance guarantees. Theorems are presented together with practical aspects of methodology and intuition to help students develop tools for selecting appropriate methods and approaches to problems in their own research.

The course includes topics in statistical theory that are now becoming important for researchers in machine learning, including consistency, minimax estimation, and concentration of measure. It also presents topics in computation including elements of convex optimization, variational methods, randomized projection algorithms, and techniques for handling large data sets.


Homework 1

Homework 2

Homework 3

Homework 4

Homework 5

Recitation Notes on Subdifferentials

Course notes.

The course notes are chapters from our book. They are available on Blackboard. DO NO DISTRIBUTE THESE CHAPTERS. Blackboard