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.
Recitation sections are lead by the TAs and are optional.
Date 
Topics 
Readings 

Supervised Learning: Classifiers 

Th 8/31 
Machine Learning: Overview Introduces key concepts 
M1 PDF 

W 9/6 
Machine Learning: Overview (continued) Introduces key concepts 

M 9/11 
Linear Regression 
M7, excluding M7.4 and M7.6 

W 9/13 
Linear Regression (continued) 

F 9/15 
Recitation: Probability Events, random variables, probabilities, pdf, pmf, cdf, mean, mode, median, variance, multivariate distributions, marginals, conditionals, Bayes theorem, independence 

M 9/18 
Logistic Regression Introduces classification methods 
M8 (stop at M8.3.7), M13.3 

W 9/20 
Perceptron Online learning algorithms 
M8.5 

F 9/22 
Recitation: Math Review Linear algebra, calculus, optimization 
cs229's linearalgebra notes


M 9/25 
Support Vector Machines Maxmargin classification and optimization 
M14.5 

W 9/27 
Kernel Methods Dual optimization, kernel trick 
M14.1, M14.2 

F 9/29 
Recitation: Recap + TBD 

M 10/2 
Decision Trees Construction, pruning, overfitting 
M2.8, M16.2 

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

F 10/6 
Recitation: TBD 

M 10/9 
Deep Learning 1 
M16.5, M27.7, M28 

W 10/11 
Deep Learning 2 

F 10/13 
Recitation: Midterm Review 

M 10/16 
Deep Learning 3 

Unsupervised Learning: Core Methods 

W 10/18 
Clustering Kmeans 
M25.1, M11 (stop at M11.4) 

F 10/20 
Fall Break 

M 10/23 
Midterm 

W 10/25 
Expectation Maximization 1 
M11.4 (stop at M11.4.8) 

F 10/27 
Recitation: TBD 

M 10/30 
Expectation Maximization 2 Kmeans 

W 11/1 
Graphical Models 1 Bayesian networks and conditional independence 
M10 

F 11/3 
Recitation: TBD 

M 11/6 
Graphical Models 2 MRFs and exact inference 
M19.1 (stop at M19.4), M19.5 

W 11/8 
Graphical Models 3 Inference 
M20 (stop at M20.3) 

F 11/10 
Recitation: TBD 

M 11/13 
Graphical Models 4 Max Sum and Max Product 

W 11/15 
Structured Prediction 1 Margin based methods, HMMs, CRFs 
M17 (stop at M17.6), M19.6, M19.7 

F 11/17 
Recitation: TBD 

M 11/27 
Structured Prediction 2 Recurrent Neural Networks 

W 11/29 
Dimensionality reduction PCA 
M12.2 

F 12/1 
Recitation: TBD 

M 12/4 
Catchup 

W 12/6 
Practical Machine Learning 

F 12/8 
NO RECITATION 

Th 12/14 
Final Exam 25pm 

