Schedule details will be filled in as class progresses See also 2014 schedule

Week Date Topic Assignments / Reading
1 9/8 Intro [slides] HW0 due Friday, 9/16
9/9 Fourth Hour (optional): Calculus Review [slides]
2 9/13 Linear regression in one variable [slides] See CS335 Python Guide
9/15 Gradient descent for linear regression [slides]
9/16 Fourth Hour (required): Intro to Python
Demo code: [html] [ipynb]
HW0 due [moodle]
HW1 out [pdf] [files] [submit]
3 9/20 Linear algebra [html] [ipynb]
9/22 Multivariate linear regression [slides] HW 2 out [pdf] [files] [submit]
4 9/27 Multivariate linear regression continued: normal equations and vectorized gradient descent
Demo: vectorized code and array slicing [html] [ipynb]
9/29 Logistic regression [slides]
5 10/4 Guest lecture: KNN and decision trees [slides]
Demo: [html] [ipynb]
10/6 Finish logistic regression
Start Nonlinearity, overfitting, regularization
HW 3 out [pdf] [files] [submit]
10/13 Nonlinearity, overfitting, regularization
scikit-learn classification demo: [html] [ipynb] [data]
7 10/18 Multiclass classification [slides]
10/20 NO CLASS HW 4 out [pdf] [files] [submit]
8 10/25 Cross-validation [slides] Project announced
10/27 Kernel Trick [slides]
Kernel trick demo: [html] [ipynb]
9 11/1 Kernel Trick continued
Neural Nets Intro
11/3 Neural Nets – Backprop [slides]
10 11/8 Neural Nets
Backprop demo: [html] [ipynb]
Neural net demo: [html] [ipynb]
Cross-entropy loss demo: [html] [ipynb]
Project proposal due [submit]
11/10 Neural Net Demos Quiz practice [pdf] [soln]
11 11/15 Movie Recommendations [files]
11/17 Movie Recommendations Take-home quiz
12 11/22 PCA [slides]
[Pandas demo]
Project milestone due Wed [submit]
13 11/29 Bayesian Classification [slides] Reading: Ng handout on generative models
12/1 K-Means Clustering and GMMs [slides] Movie predictions due [submit]
14 12/6 NO CLASS
12/8 Guest Lecture: John Foley
15 12/13 Final presentations Project report due Tue Dec 20 at noon [submit]