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 3-3.3 slides
audio
2/2 Naive Bayes ML 3.5, 5 Lab 1 hw 1 solutions [pdf] [tex] slides
audio
2/7 LDA/QDA ML 4.1-4.2 slides
audio
2/9 Linear model ML 7-7.3 Hw 1 (due Sunday 5pm) lab 2
lab2 eval
hw 2
solutions [pdf] [tex]
slides
audio
2/14 Regularization ML 7.4-7.5 slides
audio
2/16 Logistic Regression ML 8-8.3 Lab 2 slides
audio
2/21 Logistic Reg ML 8.5-8.6.2 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 9.3-9.4.2, 9.6-9.7 Lab 3 slides
3/7 Neural Networks ML 16.5-16.5.6 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 11-11.4 slides
3/30 Expectation Maximization ML 11.4-11.6 slides
4/4 PCA ML 12 slides
4/6 Reinforcement learning Lab 5 slides
4/11 SVM 14-14.4 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 7:30-10 Attendance mandatory Reservoir Computing
LDA
DQN
Bandit problems
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