Course Schedule
Lecture  Date  Topic  Readings  Notes 

1  9/3  Course overview Supervised learning 

Supervised Learning  
2  9/5  Linear Regression (Part 1)  UML 9.2 ESL 3.2 

3  9/10  Linear Regression (Part 2)  UML 11.2 ESL 3.4, 7.10 

4  9/12  Linear Classification (Part 1) Gradient Descent 
UML 12 ESL 4.14.2, 4.4 

5  9/17  Linear Classification (Part 2) Logistic Regression 
UML 9.3  
6  9/19  Support Vector Machine (Part 1) Lagrange Duality 
ESL 12.112.3 CML 7.7 

7  9/24  Support Vector Machine (Part 2)  ESL 12.112.3 UML 15 

8  9/26  Kernels and Neural Networks  ESL 5.8, 6.3, 6.7 UML 16, 20 DL Book 6 
HW1 Due HW2 Out 
9  10/1  Neural Networks  DL Book 9  
10  10/3  Optimization and Training  DL Book 8  
Learning Theory  
11  10/8  Learning Theory (Part 1)  UML 6  
12  10/10  Model Selection Guest Lecture by Akshay krishnamurthy 
UML 11  HW2 Due 
13  10/15  Learning Theory (Part 2) Midterm Review 
UML 26  
10/17  
Ensemble methods  
14  10/22  Decision Trees  UML 10.2, 18  
15  10/24  AdaBoost  Boosting Book 3 5  HW3 Out 
16  10/29  Gradient Boosting BiasVariance Tradeoff 
ESL 10.9 10.10  Haiku 
17  10/31  Bagging Random Forest 
ESL 15  
Unsupervised Learning  
18  11/5  PCA  ESL 14.5  
19  11/7  Kernel PCA Maximum Likelihood Estimation 
ESL 14.5  HW3 Due HW4 Out 
20  11/12  Gaussian Mixture Model ExpectationMaximization 
ESL 8.5  
21  11/14  Variational Autoencoder  
22  11/19  Generative Adversarial Nets  
Interactive Learning  
23  11/21  Online Learning (Part 1) Halving and Percepron 
HW4 Due HW5 Out 

24  11/26  Online Learning (Part 2) Multiplicative Weights (MW) 
Notes by J.K. MW Not Covered in 2nd Exam 

11/28  Thanksgiving break  
25  12/3  Societal Consequences  
26  12/5  Final Review  
12/10  
12/13 