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. Logistic regression Lecture 3 slides

Jan.17 More on logistic regression. Introduction to kernels. Lecture 4 slides
Jan.19 More on kernels. Introduction to Support vector machines. Lecture 5 slides
Bishop, Sec. 4.3
Jan.24 More on kernels. Supoort vector machines Lecture 6 slides
Bishop, Sec. 5.1, 5.2, 5.3, 5.4, 5.5
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 Hidden Markov Models: Inference and Learning Lecture 10 slides
Postponed Learning methods for graphical models. Latent variables. Lecture 10 slides
Feb. 9 More on learning in directed graphical models Lecture 11 slides
Feb.14 No class (watch Tibshirani invited talk at NIPS)
Feb.16 Undirected graphical models Lecture 12 slides
Feb.21 Dimensionality reduction: PCA, kernel PCA, LLE, MDS... Lecture 13 slides
Bishop 12.1, 12.3 (or equivalent)
Locally Linear Embeddings (optional)
Feb.23 TBD 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