Statistical Machine Learning

10-702/36-702, Spring 2013

Larry Wasserman, Aarti Singh

Class Assistant: Michelle Martin
Teaching Assistants: Akshay Krishnamurthy, Yifei Ma


Lecture:
Date and Time: Monday and Wednesday, 1:30 - 2:50 pm
Location: Baker Hall A51

Recitation hours: Akshay, Yifei: Thursdays, 5-6 pm, Porter Hall 125C

TA Office hours: Akshay, Yifei: Mondays, 3-4 pm, 8th floor GHC commons

Professor Office hours: Larry: Wednesdays, 3-4 pm, Baker Hall 228a
Aarti: Tuesdays, 2:30-3:30 pm, Gates 8207

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.

Handouts:

Syllabus

 Course notes. The course notes are chapters from Professor Wasserman's book. They are available on Blackboard. DO NO DISTRIBUTE THESE CHAPTERS. Blackboard