Schedule is subject to change
Week  Date  Topic  Assignments / Reading 

1  9/4  Intro [slides]  HW 0 part 1 due Tue 9/9 matlab exercise due Fri 9/12 by moodle 
9/5  Fourth hour: Optional calculus review [slides]  
2  9/9  Linear regression in one variable [slides]  Read pp. 1–7 of Ng handout 
9/11  Gradient descent for linear regression [slides]  
9/12  Fourth hour (required): Intro to MATLAB  HW 1 due Fri 9/19 [problem_3.m] [moodle]  
3  9/16  Linear algebra review [slides] [demo.m]  
9/18  Multivariate Linear Regression [slides]  
9/19  Fourth hour: MATLAB help, HW1 (optional)  HW 2 pdf HW 2 files Exercises due Fri 9/26 [moodle] Problems due Wed 10/1 [moodle] 

4  9/23  Geometry of HighDimensional Linear Functions  
9/25  Normal Equations and Vectorized Gradient Descent  
9/26  Optional Fourth Hour: HW 2, linear algebra  
5  9/30  Feature Normalization / Logistic Regression [slides]  
10/2  Logistic Regression  HW 3 pdf HW 3 files due Wed 10/15 [moodle]  
6  10/7  Nonlinearity, Overfitting, Regularization [slides]  
10/9  Multiclass Classification [slides] Crossvalidation [slides] 

7  10/14  No Class: MidSemester Break  
10/16  Guest Lecture: Weka Tutorial by Kevin Winner  Quiz on moodle, due Tues 10/12  
10/17  No fourth hour: takehome quiz  
8  10/21  Support Vector Machines [slides]  Reading: Ng handout 
10/23  Kernel SVMs [slides] [demos]  HW4 pdf HW4 files due Fri 10/31 [moodle]  
9  10/28  Gaussian Kernel, Intro to Neural networks  Quiz 1 returned [soln] 
10/30  Neural networks [slides] [notes]  Project guidelines  
10  11/4  Backprop  
11/6  Movie Recommendations [slides] [notes] [code and data]  Project proposal due 

11  11/11  Movie recommendations  
11/13  Principal Components Analysis (PCA) [slides] [demos]  
11/14  Probability review  Stanford probability review handout Movie predictions due Nov 14 at 11:55pm [moodle] Work on projects 

12  11/18  Bayesian Reasoning and Naive Bayes [slides]  
11/20  Naive Bayes Discriminative vs. Generative Models 
Ng handout on generative models Ng and Jordan paper on discriminative vs. generative models Work on projects! 

13  11/25  Clustering, Kmeans, Mixture of Gaussians [slides]  Project milestone due Nov 26 at noon [moodle] 
11/27  No class: Thanksgiving  
14  12/2  No class  Takehome quiz 
12/4  Guest Lecture: Marc Maier from MassMutual Data Science  Work on projects. Skype office hours.  
15  12/9  No class  
12/10  Final presentations from 911am  
16  FINALS  Project report due Dec 16 at noon 