I am self-studying this course and working through material for my own edification.
http://www.cs.cmu.edu/~ninamf/courses/601sp15/index.html
Description:
Machine Learning is concerned with computer programs that automatically improve their performance through experience (e.g., programs that learn to recognize human faces, recommend music and movies, and drive autonomous robots). This course covers the theory and practical algorithms for machine learning from a variety of perspectives. We cover topics such as Bayesian networks, decision tree learning, Support Vector Machines, statistical learning methods, unsupervised learning and reinforcement learning. The course covers theoretical concepts such as inductive bias, the PAC learning framework, Bayesian learning methods, margin-based learning, and Occam's Razor. Short programming assignments include hands-on experiments with various learning algorithms. This course is designed to give a graduate-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in machine learning.
Sample of topics (non-exhaustive):
- Decision trees
- (Gaussian) naive bayes
- Logistic/linear regression
- Learning theory
- Graphical models
- Bayesian networks
- EM
- Boosting
- Kernels
- SVM
- Active learning
- Dimensionality reduction