Video of the lectures is available via the echo360 portal of the course. A link is provided on Canvas and Piazza.

Topics Reading Assignments
Week 1: August 23,25
Tuesday Course introduction ( slides). Sections 1.1 and 1.2 in the textbook
Thursday Course introduction (continued). Sections 1.1 and 1.2 in the textbook Assignment 1 is available.
Week 2: August 30, Sept 1
Tuesday Linear models ( slides). Short intro to LaTex and python [ notes ]. Chapter 1, and Section 3.1 in the textbook
Thursday Linear models and the perceptron algorithm (cont). Chapter 1, and Section 3.1 in the textbook Assignment 2 is available.
Week 3: September 6,8
Tuesday code for the perceptron. Linear regression ( slides). Chapter 3.2
Thursday Logistic regression ( slides). Chapter 3.3
Week 4: September 13,15
Tuesday Overfitting ( slides) Chapters 2.3,4.1
Thursday Regularization and model selection ( slides) Chapter 4
Week 5: September 20,22
Tuesday Model selection and cross validation (continued). Code for cross validation in scikit-learn Chapter 4 Assignment 3 is available.
Thursday Discussion of classifier evaluation and metrics for classifier accuracy; here's the code for computing/plotting ROC curves. Short intro to large margin classification ( slides) Chapter e-8
Week 6: September 27,29
Tuesday Large margin classification: support vector machines ( slides) Chapter e-8
Thursday The dual for the hard margin and soft margin SVM ( slides); svm demo; Expressing SVMs in terms of error + regularization ( slides) Chapter e-8
Week 7: October 4,7
Tuesday SVMs for unbalanced data ( slides) Nonlinear classification with kernels ( slides) Chapter e-8 Assignment 4 is available.
Thursday Kernels (continued); model selection using grid search Chapter e-8
Week 8: October 11,13
Tuesday Model selection ( slides). Multi-class classification ( slides), a demo of multi-class classification Chapter 4.3.3
Thursday Neural networks ( slides) Chapter e-7
Week 9: October 18,20
Tuesday Neural networks (continued); neural network demo Chapter e-7 Assignment 5 is available.
Thursday Neural networks (continued); deep learning ( slides) Chapter e-7
Week 10: October 25,27
Tuesday Deep learning (continued) Chapter e-7
Thursday Theano. Features ( slides) Chapter e-9
Week 11: November 1,3
Tuesday Feature selection ( slides); feature selection demo. Introduction to Variable and Feature Selection.
Thursday Principal components analysis (PCA) ( slides) demo of pca Chapter e-9 Assignment 6 is available.
Week 12: November 8,10
Tuesday Nearest neighbor methods ( slides); demo. Chapter e-6
Thursday Clustering ( slides) kmeans demo Chapter 10 in introduction to statistical learning
Week 13: November 15,17
Tuesday Naive Bayes classification ( slides); demo.
Thursday Intro to computational learning theory ( slides) Chapter 1.3 in the textbook
Thanksgiving break
Week 14: November 29, December 1
Tuesday The VC dimension ( slides); Least squares regression revisited ( slides) Chapter 2.1, 2.2 in the textbook
Thursday Ensemble models ( slides); demo.
Week 15: December 6,8
Tuesday Course summary ( slides)
Thursday Poster session
Week 16
Tuesday Submit final reports