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 Linear models  
Sep 5 Online learning  
Sep 10 Perceptron HW 1 due, HW2 released
Sep 12 Perceptron (continued) Project info due
Sep 17 Computational learning theory  
Sep 19 Computational learning theory (continued)  
Sep 24 Computational learning theory: Agnostic learning HW2 due, HW 3 released
Sep 26 Buffer lecture Project proposal due
Oct 1 Buffer lecture 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: VC dimensions HW 4 released
Oct 17 Boosting, ensembles  
Oct 22 Support Vector Machines  
Oct 24 SVMs (continued)  
Oct 29 Risk minimization HW4 due, HW 5 released
Oct 31 Bayesian Learning  
Nov 5 Naive Bayes Classification Project intermediate report due
Nov 7 Naive Bayes (continued)  
Nov 12 Logistic Regression HW 5 due
Nov 14 Neural Networks  
Nov 19 Neural Networks (continued)  
Nov 21 Nearest Neighbor Classification  
Nov 26 Buffer lecture 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)