http://www.cs.cmu.edu/~avrim/ML07/coursedesc.html

DESCRIPTION

This course will focus on theoretical aspects of machine learning. We will examine questions such as: What kinds of guarantees can one prove about learning algorithms? What are good algorithms for achieving certain types of goals? Can we devise models that are both amenable to mathematical analysis and make sense empirically? What can we say about the inherent ease or difficulty of learning problems? Addressing these questions will combine statistics, complexity theory, information theory, cryptography, game theory, and empirical machine learning research.

PREREQUISITES:
Either 15-681/781 Machine Learning, or 15-750 Algorithms, or a strong Theory/Algorithms background.

TEXT:
Kearns and Vazirani, "An introduction to computational learning theory" plus papers and notes for topics not in the book. (Roughly half of the topics are in the book)

METHOD OF EVALUATION:
Grading will be based on 6 homework assignments, a final exam (worth about 2 homeworks), class participation, and a small class project. Students from time to time will also be asked to help with the grading of assignments.