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 led by the TAs and are optional.
Date 
Topics 
Readings 

Supervised Learning: Classifiers 

M 1/29 
Machine Learning: Overview Introduces key concepts 
M1 PDF 

W 1/31 
Machine Learning: Overview (continued) Introduces key concepts 

M 2/5 
Linear Regression 
M7, excluding M7.4 and M7.6 

W 2/7 
Linear Regression (continued) 

F 2/9 
Recitation: Probability Events, random variables, probabilities, pdf, pmf, cdf, mean, mode, median, variance, multivariate distributions, marginals, conditionals, Bayes theorem, independence 

M 2/12 
Logistic Regression Introduces classification methods 
M8 (stop at M8.3.7), M13.3 

W 2/14 
Perceptron Online learning algorithms 
M8.5 

F 2/16 
Recitation: Math Review Linear algebra, calculus, optimization 
cs229's linearalgebra notes


M 2/19 
Support Vector Machines Maxmargin classification and optimization 
M14.5 

W 2/21 
Kernel Methods Dual optimization, kernel trick 
M14.1, M14.2 

F 2/23 
Recitation: Recap + TBD 

M 2/26 
Decision Trees Construction, pruning, overfitting 
M2.8, M16.2 

W 2/28 
Boosting Ensemble methods 
M16.4, M16.6 

F 3/2 
Recitation: PyTorch Intro 

M 3/5 
Deep Learning 1 
M16.5, M27.7, M28 

W 3/7 
Deep Learning 2 

F 3/9 
Recitation: Midterm Review 

M 3/12 
Deep Learning 3 

W 3/14 
Midterm  
F 3/16 
No Recitation 

Unsupervised Learning: Core Methods 

M 3/26 
Clustering Kmeans 
M25.1, M11 (stop at M11.4) 

W 3/28 
Expectation Maximization 1 
M11.4 (stop at M11.4.8) 

F 3/30 
Recitation: Review Midterm Answers 

M 4/2 
Expectation Maximization 2 

W 4/4 
Dimensionality reduction PCA 
M12.2 

F 4/6 
Recitation: Deep Learning Examples 

M 4/9 
Graphical Models 1 Bayesian networks and conditional independence 
M10 

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

F 4/13 
Recitation: TBD 

M 4/16 
Graphical Models 3 Inference 
M20 (stop at M20.3) 

W 4/18 
Graphical Models 4 Max Sum and Max Product 

F 4/20 
Recitation: Graphical Models 

M 4/23 
Structured Prediction 1 Margin based methods, HMMs, CRFs 
M17 (stop at M17.6), M19.6, M19.7 

W 4/25 
Structured Prediction 2 Recurrent Neural Networks 

F 4/27 
Recitation: TBD 

M 4/30 
Practical Machine Learning 

W 5/2 
Final Review 

F 5/4 
Recitation: TBD 

M 5/14 
Final Exam 9 am  12 pm 

