Instructor: Avrim Blum (GHC 8111).
Office Hours: TBD.
Credits: 12 Units
Course Description: This course will focus on theoretical aspects of machine learning. We will examine questions such as: What kinds of guarantees can we prove about machine learning algorithms? Can we design algorithms for interesting learning tasks with strong guarantees on accuracy and amounts of data needed? (Why) is Occam's razor a good idea and what does that even mean? What can we say about the inherent ease or difficulty of learning problems? Can we devise models that are both amenable to theoretical analysis and make sense empirically? Addressing these questions will bring in connections to probability and statistics, online algorithms, game theory, complexity theory, information theory, cryptography, and empirical machine learning research.
Prerequisites: A Theory/Algorithms background or a Machine Learning background.
Evaluation and Responsibilities: Grading will be based on 6 homework assignments, class participation, a small class project, and a take-home final (worth about 2 homeworks). Students from time to time will also be asked to help with the grading of assignments.
Text (recommended): 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)
Web page: http://www.cs.cmu.edu/~avrim/ML12/.
See also the Spring 2010 version of this course.