The schedule below is tentative and both the order and the content may change as the semester goes along. The slides and resources for each lecture will be updated after the class.

Date Lectures  
Aug 20 Introduction and motivation HW 0 released
Aug 22 Supervised learning: The setup  
Aug 27 Decision trees HW 0 due, HW 1 released
Aug 29 Decision trees (continued)  
Sep 3 Decision trees (continued)  
Sep 5 Linear models  
Sep 10 Linear models (continued) HW 1 due, HW2 released
Sep 12 Online learning Project info due
Sep 17 Online Learning  
Sep 19 Perceptron  
Sep 24 Perceptron (continued) HW2 due, HW 3 released
Sep 26 Least Mean Squares Regression Project proposal due
Oct 1 Computational learning theory HW 3 due
Oct 3 Midterm exam (in class)  
Oct 8 Fall break. No class.  
Oct 10 Fall break. No class.  
Oct 15 Computational learning theory: PAC learning  
Oct 17 Computational learning theory: Agnostic learning  
Oct 22 Computational learning theory: VC dimensions HW 4 release
Oct 24 Boosting, ensembles  
Oct 29 Support Vector Machines  
Oct 31 SVMs (continued)  
Nov 5 Risk minimization  
Nov 7 Bayesian Learning HW4 due, HW 5 released
Nov 12 Naive Bayes Classification Project intermediate report due
Nov 14 Logistic Regression  
Nov 19 Neural Networks  
Nov 21 Neural Networks (continued) HW 5 due
Nov 26 Nearest Neighbor Classification HW 6 released
Nov 28 Thanksgiving! No class.  
Dec 3 Buffer lecture HW 6 due
Dec 5 Practical advice for building ML applications  
Dec 9 Final exam (8:00 – 10:00 am) Project final report due

Additional Topics (which we may cover if time permits)