Due date Topic Readings Assignment Additional files Slides
1/17 Overview ML 2 (assumed background) Piazza, ipython notebook slides
1/19 Learning ISLR 2, ML 1 ISLR 2 slides
1/24 Probability, Cross Validation ML 2 distributions slides (some math missing) slides (black background)
1/26 Probability Lab lab 1
1/31 Bayesian Concept learning ML 3-3.3 slides
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
2/9 Lab 2 NB, KNN Hw 1 (due Sunday 5pm) lab 2
lab2 eval
HW 2
solutions [pdf] [tex]
2/14 Linear model ML 7-7.3 slides
2/16 Regularization ML 7.4-7.5 Lab 2 slides
2/21 Logistic Regression ML 8-8.3 Kaggle slides
2/23 Lab 3 Linear/Logistic Regression Hw 2 lab 3
2/28 Logistic Reg ML 8.5-8.6.2 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 NN slides
BackProp slides
3/9 Lab 4 NN delta learning
back prop
lab 4
3/14 Test review
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 Lab 5 Lab 5
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
4/20 Lab 6 SVM Lab 6
4/25 Clustering ML 25 Hw 4
4/27 Reinforcement learning ML 16.4-16.6 Lab 6 slides
5/2 Boosting slides
5/4 test review Kaggle
5/9 Exam 1:30-4
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