B Machine Learning (COMP-652)
Machine Learning (COMP-652)
Winter 2014

Lecture Schedule

Date Topic Materials
Jan.6 Introduction Lecture 1 slides
Bishop, Sec. 1.1 If you need to catch up on the math, a brief probability review and linear algebra review from Stanford University
Jan. 8 More on linear models. Regularization. Lecture 2 slides
Bishop, Sec. 3.1, 3.2
Jan.13 Maximum likelihood and Bayesian learning Lecture 3 slides
Bishop, Sec. 3.1, 3.3
Jan. 15 Non-linear methods: Kernels Lecture 4 slides
Jan.20 More on non-linear methods. Optimization approaches.
Jan. 22 The notion of margin. Max-margin methods
Jan.27 Gaussian processes
Jan. 29 PCA Lecture 8 slides
Feb. 3 Computational learning theory Lecture 9 slides
Feb. 5 More computational learning theory See slides from above
Feb.10 Mixture models Lecture 11 slides
Feb.12 Graphical models: Bayes nets Lecture 12 slides
Feb.17 Exact inference for graphical models
Feb.19 More on inference methods for graphical models. Learning in directed graphical models Lecture 14 slides
Feb.24 Approximate inference methods
Feb.26 Maximum likelihood learning and Bayesian learning for graphical models.
Mar.10 Learning with latent variables. EM
Mar.12 More on unsupervised learning
Mar.17 Deep belief methods
Mar.19 More on deep models
Mar.24 Analyzing temporal and sequence data
Mar.26 In-class midterm exam. Covers lectures up to March 19
Mar.31 Spectral methods for time series

Apr. 2 Temporal-difference learning
Apr. 7 More on reinforcement learning
Apr. 9 More on reinforcement learning. Wrap-up