For those who prefer Chris Bishop's Pattern Recognition and Machine Learning, I will list the corresponding readings from this textbook. These readings will be prefixed by "B". Optional readings will be written in italics.
Date 
Topics 
Readings 

Supervised Learning: Classifiers 

W 9/7 
Machine Learning Foundations with Linear Regression Introduces key concepts with linear regression 
M1 PDF 

M 9/12 
Linear Regression (Continued) 
M7, excluding M7.4 and M7.6 

W 9/14 
Logistic Regression Introduces classification methods 
M8 (stop at M8.3.7), M13.3 

M 9/19 
Perceptron Online learning algorithms 
M8.5 

W 9/21 
Support Vector Machines Maxmargin classification and optimization 
M14.5 

M 9/26 
Kernel Methods Dual optimization, kernel trick 
M14.1, M14.2 

W 9/28 
Decision Trees Construction, pruning, overfitting 
M2.8, M16.2 

M 10/3 
Catchup day 


W 10/5 
Boosting Ensemble methods 
M16.4, M16.6 

M 10/10 
Deep Learning 1 
M16.5, M27.7, M28 

W 10/12 
Deep Learning 2 

M 10/17 
Midterm 

Unsupervised Learning: Core Methods 

W 10/19 
Probability 
M2 

Th 10/20 
Clustering Kmeans 
M25.1, M11 (stop at M11.4) 

M 10/24 
Expectation Maximization 1 
M11.4 (stop at M11.4.8) 

W 10/26 
Expectation Maximization 2 Kmeans 

M 10/31 
Graphical Models 1 Bayesian networks and conditional independence 
M10 

W 11/2 
Graphical Models 2 MRFs and exact inference 
M19.1 (stop at M19.4), M19.5 

M 11/7 
Graphical Models 3 Inference 
M20 (stop at M20.3) 

W 11/9 
Graphical Models 4 Max Sum and Max Product 

M 11/14 
Structured Prediction 1 Margin based methods, HMMs, CRFs 
M17 (stop at M17.6), M19.6, M19.7 

W 11/16 
Structured Prediction 2 Recurrent Neural Networks 

M 11/28 
Dimensionality reduction PCA 
M12.2 

W 11/30 
Nanyun Peng: Deep model + graphical models 

M 12/5 
Ilya Shpitser: Causal Inference 

W 12/7 
Practical Machine Learning 

F 12/18 
NO FINAL EXAM 

