Course Schedule (Tentative)
Lecture  Date  Topic  Readings  Notes 

1  1/27  Course overview  Lecture 1  
Supervised Learning  
2  Linear Regression (Part 1)  UML 9.2 ESL 3.2 
Lecture 2  
3  2/3  Linear Regression (Part 2)  UML 11.2 ESL 3.4, 7.10 
Lecture 3 
4  Linear Classification Gradient Descent 
UML 12 ESL 4.14.2, 4.4 
Lecture 4  
5  2/10  Logistic Regression  UML 9.3  Lecture 5 
6  Support Vector Machine (Part 1) Lagrange Duality 
UML 15 ESL 12.112.3 
Lecture 6 Scribble 6 

2/16  HW 1 Due  
7  2/17  Support Vector Machine (Part 2)  ESL 12.112.3 UML 15 
Lecture 7 
8  Kernels  ESL 5.8, 6.3, 6.7 UML 16, 20 
Lecture 8 Scribble 8 

9  2/24  Neural Networks  DL Book 6 9  
10  Optimization and Training  DL Book 8  
3/1  HW 2 Due  
Learning Theory  
11  3/2  Learning Theory (Part 1)  UML 6  
12  Learning Theory (Part 2)  UML 11  
3/9  
13  3/16  Midterm Review  UML 26  
3/20  HW 3 Due  
3/23  
Ensemble methods  
14  3/30  Decision Trees  UML 10.2, 18  
15  AdaBoost  Boosting Book 3 5  
16  4/6  Gradient Boosting BiasVariance Tradeoff 
ESL 10.9 10.10  
17  Bagging Random Forest 
ESL 15  
4/12  HW 4 Due  
Unsupervised Learning  
18  4/13  PCA  ESL 14.5  
19  Kernel PCA Maximum Likelihood Estimation 
ESL 14.5  
20  4/20  Gaussian Mixture Model ExpectationMaximization 
ESL 8.5  
21  Generative Adversarial Nets Variational Autoencoder 

4/26  HW 5 Due  
Interactive Learning  
22  4/27  Online Learning (Part 1) Halving and Percepron 

23  Online Learning (Part 2) Multiplicative Weights (MW) 

5/4 