CS 5970 – Computational Learning Theory

Flyer for the Course

Here you can find a flyer for the course for the fall of 2022.

Course Description

Topics of machine learning theory. Learning using membership queries, equivalence queries, version spaces, decision trees, perceptrons. Probably approximately correct (PAC) learning, Occam algorithms, VC-dimension, sample sizes for distribution-independent learning. Representation issues, proper learning, reductions, intractability, learning in the realizable case, agnostic learning. Noise models, statistical queries, PAC learning under noise. Adversarially robust learning against poisoning attacks and against adversarial examples. Distribution-specific learning and evolvability. Online learning and learning with expert advice in the mistake bound model. Weak and strong learning (boosting).

Times Offered

Below is a list of times when CS 5970 – Computational Learning Theory has been (or is scheduled to be) offered and taught by me.