[Colloquium] New Course: Large Scale Learning, CMSC 35900

[Julia MacGlashan]

NEW COURSE:  CMSC 35900 - Large Scale Learning, Spring 2009

 

Instructors:  Sham Kakade (sham at tti-c.org), Greg Shakhnarovich
(greg at tti-c.org) 

 

Time and place:  TT 1:30-2:50pm, TTI-C room 530, 6045 S. Kenwood Ave., 5th
floor

 

Course website:
http://ttic.uchicago.edu/~gregory/courses/LargeScaleLearning/

 

 

The course will focus on theory and practice of working with large data
sets, characterized by large numbers of data points and/or high dimensions.
We will cover a set of computational tools that allow efficient storage,
search and inference in such data sets, as well as theoretical results
pertaining to these tools. We consider both statistical and computational
efficiency and limitations of the methods covered, and discuss data
structures and algorithms for implementing these methods in practice.

 

The major topics discussed in the course will include the following:

 

* Dimensionality reduction, including random projections, spectral methods
and embeddings.

* Efficient indexing and search, including locality-sensitive hashing,
metric trees

* Large-scale non-parametric methods, including locally-weighted regression,
example-based density estimation

* Unsupervised statistical learning tasks, including clustering

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