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 

Th 8/29 
Machine Learning: Overview Introduces key concepts 
M1 PDF 

W 9/4 
Linear Regression Our first algorithm 
M7, excluding M7.4 and M7.6 

F 9/6 
Recitation: Probability Events, random variables, probabilities, pdf, pmf, cdf, mean, mode, median, variance, multivariate distributions, marginal\ s, conditionals, Bayes theorem, independence 

M 9/9 
Linear Regression: continued 

W 9/11 
Logistic Regression Introduces classification methods 
M8 (stop at M8.3.7), M13.3 

F 9/13 
Linear Algebra Review Basic properties of matrices, eigenvalue decompositions, singular value decompositions 

M 9/16 
Perceptron Online learning algorithms 
M8.5  
W 9/18 
Support Vector Machines Maxmargin classification and optimization 
M14.5 

F 9/20 
Recitation: Math Review Linear algebra, calculus, optimization 
cs229's linearalgebra notes


M 9/23 
Kernel Methods Dual optimization, kernel trick 
M14.1, M14.2 

W 9/25 
Decision Trees Construction, pruning, overfitting 
M2.8, M16.2 

F 9/27 
Recitation: Recap + TBD 

M 9/30 
Boosting Ensemble methods 
M16.4, M16.6 

W 10/2 
Deep Learning 1 
M16.5, M27.7, M28 

F 10/4 
Recitation: PyTorch Intro 

M 10/7 
Deep Learning 2 

W 10/9 
Deep Learning 3 

F 10/11 
Recitation: Midterm Review 

Unsupervised Learning: Core Methods 

M 10/14 
Clustering Kmeans 
M25.1, M11 (stop at M11.4) 

W 10/16 
Expectation Maximization 1 
M11.4 (stop at M11.4.8) 

F 10/18 
Fall Break 

M 10/21 
Midterm 

W 10/23 
Expectation Maximization 2 Kmeans 

F 10/25 
Recitation: TBD 

M 10/28 
Graphical Models 1 Bayesian networks and conditional independence 
M10 

W 10/30 
Graphical Models 2 MRFs and exact inference 
M19.1 (stop at M19.4), M19.5 

F 11/1 
Recitation 

M 11/4 
Graphical Models 3 Inference 
M20 (stop at M20.3) 

W 11/6 
Graphical Models 4 Max Sum and Max Product 

F 11/8 
Recitation: TBD 

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

W 11/13 
Structured Prediction 2 Recurrent Neural Networks 

F 11/15 
Recitation 

M 11/18 
Dimensionality reduction PCA 
M12.2 

W 11/20 
Fairness, Accountability, Transparency and Ethics of ML 

F 11/22 
Recitation 

M 12/2 
Practical Machine Learning 

W 12/4 
TBD 

F 12/6 
Final Review 

12/11 
Final Exam Wednesday, December 11 9 AM10:15AM (75 minutes) 


