Machine learning studies automatic methods for learning to make accurate predictions, to understand patterns in observed features and to make useful decisions based on past observations.
This course introduces theoretical machine learning, including mathematical models of machine learning, and the design and rigorous analysis of learning algorithms.
Here is a tentative list of topics. (Bullets do not correspond precisely to lectures.)
PAC learning basics, and PAC learning in Neural Networks
Boosting and unsupervised boosting
Graphical model basics
Spectral methods: a case study of consistent algorithms
Take Home Midterm (20%)
Take Home Final (20%)
Office hours: Wednesday 6:15-7:15pm in IRB 4204