Date and Time: Monday and Wednesday, 1:30 - 2:50 pm
Location: Baker Hall A51
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
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
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.
The course notes are chapters from Professor Wasserman's book.
They are available on Blackboard.
DO NO DISTRIBUTE THESE CHAPTERS.