Statistical Machine Learning
10-702/36-702, Spring 2012
Larry WassermanBaker Hall 228a
Class Assistant: Sharon Cavlovich
Teaching Assistants:
Martin Azizyan ,
Sivaraman Balakrishnan
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Lecture: |
Date and Time: Tuesday and Thursday, 12:00 - 1:20 pm Location: SH 125 |
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| TA Office hours: |
Sivaraman: Wednesdays 3-4 (common area outside GHC 8014) Martin: Thursdays 3-4 (common area outside GHC 8015) |
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| Professor Wasserman's Office hour: |
Tuesdays 1:30-2:30 Baker Hall 228a |
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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. |
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