Due date  Topic  Readings  Assignment  Additional files  Slides 

1/17  Overview  ML 2 (assumed background)  Piazza  slides  
1/19  Learning  ML 1  learning video  slides  
1/24  PAC learning  FML2 or Mitchell  slides (math missing) slides (black background)  
1/26  VC dim, Rademacher  FML3  lab 1
lab 1 soln 
slides  
1/31  Bayesian Concept learning  ML 33.3  slides
audio 

2/2  Naive Bayes  ML 3.5, 5  Lab 1  hw 1 solutions [pdf] [tex]  slides
audio 
2/7  LDA/QDA  ML 4.14.2  slides
audio 

2/9  Linear model  ML 77.3  Hw 1 (due Sunday 5pm)  lab 2
lab2 eval hw 2 solutions [pdf] [tex]  slides
audio 
2/14  Regularization  ML 7.47.5  slides
audio 

2/16  Logistic Regression  ML 88.3  Lab 2  slides
audio 

2/21  Logistic Reg  ML 8.58.6.2  Kaggle  slides
audio 

2/23  TBD  Hw 2  Lab 3  
2/28  Bayesian Linear/Logistic  ML 7.6, 8.4  slides  
3/2  General Linear Models  ML 9.39.4.2, 9.69.7  Lab 3  slides  
3/7  Neural Networks  ML 16.516.5.6  slides  
3/9  Test Review  Lab 4  
3/14  Deep NN  ML 28.3  slides  
3/16  Test  solutions  
3/26  Due dates 
Kaggle
Lab 4 

3/28  Gaussian Mixture  ML 1111.4  slides  
3/30  Expectation Maximization  ML 11.411.6  slides  
4/4  PCA  ML 12  slides  
4/6  Reinforcement learning  Lab 5  slides  
4/11  SVM  1414.4  Kaggle  slides  
4/13  Kernel Methods  14.5, SVM tutorial  Lab 5  hw 4
[tex] 
slides 
4/18  SVM  14.5, SVM tutorial  slides  
4/20  Clustering  ML 25  Lab 6  
4/25  Presentations  Hw 4  Conditional Random Fields
Collaborative Filtering 

4/27  Presentations  Lab 6  Boltzmann Machines
Markov Random Fields 

5/2  Presentations  Apriori algorithm
SLAM 

5/4  Presentations  Kaggle  Community Detection
Boosting 

5/11  Presentations 7:3010  Attendance mandatory  Reservoir Computing
LDA DQN Bandit problems 