Theoretical foundations of machine learning. Topics include concentration of measure, the PAC model, uniform convergence bounds and VC dimension. Possible topics include online learning, learning with expert advice, multiarmed bandits and boosting. CSE 103 and CSE 101 or similar course recommended.
Prerequisites:
graduate standing in CSE or consent of instructor.
New Spring 2013