Stat 928, Spring 2011

Statistical Learning Theory


Statistical learning theory studies the statistical aspects of machine learning and automated reasoning, through the use of (sampled) data. In particular, the focus is on characterizing the generalization ability of learning algorithms in terms of how well they perform on ``new'' data when trained on some given data set. The focus of the course is on: providing the the fundamental tools used in this analysis; understanding the performance of widely used learning algorithms (with a focus on regression and classification); understanding the ``art'' of designing good algorithms, both in terms of statistical and computational properties. Potential topics include: concentration of measure; empirical process theory; online learning; stochastic optimization; margin based algorithms; feature selection; regularization; PCA.


The course is appropriate for a graduate student with some background in statistics and machine learning. The course will assume a basic level of mathematical maturity, so please contact the instructor if you have concerns.


Homework sets, readings, and a project.


Sham Kakadeskakade at

Time and location:

Time:MW : 3 - 4:30
Location: G90 JMHH


Notes will be posted for each lecture.

Schedule and notes: