B Machine Learning (COMP-652 and ECSE-608)
Machine Learning (COMP-652 and ECSE-608)
Winter 2017

Lecture Schedule

Date Topic Materials
Jan. 5 Introduction. Linear models. Lecture 1 slides
Bishop, Sec. 1.1, 3.1, 3.2 (or equivalent)
If you need to catch up on the math, a brief probability review and linear algebra and matrix calculus review from Stanford University; also, Bishop appendix B,C.
Jan.10 More on linear models. Overfitting. Regularization. Lecture 2 slides
Bishop, Sec. 3.1, 3.2
Jan.12 More on Bayesian and maximum likelihood fitting Lecture 3 slides

Jan.17 Logistic regression Lecture 4 slides
Jan.19 Introduction to kernels. Kernel-based logistic regression. Lecture 5 slides
Jan.24 More on kernels. Supoort vector machines Lecture 6 slides
Jan.26 Active learning Lecture 7 slides
Jan.31 Learning with structured data. Introduction to graphical models via mixture models. Lecture 8 slides
Feb. 2 Representational power of directed graphical models. Inference methods Lecture 9 slides
Feb. 7 Learning methods for graphical models. Latent variables. Lecture 10 slides
Feb. 9 Undirected graphical models. representational power Lecture 11 slides
Feb.14 Inference and learning in undirected graphical models Lecture 12 slides
Feb.16 Boltzmann machines Deep belief networks Lecture 13 slides
Feb.21 More on deep learning (supervised models) Lecture 14 slides
Feb.23 Generative adversarial networks Lecture 15 slides
Mar. 7 Time series analysis. Latent variable models for time series analysis. Lecture 16 slides
Mar. 9 Spectral methods for time series. Lecture 17 slides
Mar.14 Other types of time series analysis Lecture 18 slides
Mar.16 Unsupervised learning: a latent variable perspective Lecture 19 slides
Mar.21 Dimensionality reduction: PCA, kernel PCA, autoencoders. Lecture 20 slides
Mar.23 Autoencoders. Variational inference Lecture 21 slides
Mar.28 In-class midterm exam
Mar.30 Computational learning theory Lecture 22 slides
Apr. 4 More on computational learning theory Lecture 23 slides
Apr. 6 Reinforcement learning for prediction Lecture 24 slides
Apr.11 Reinforcement learning for control Lecture 25 slides