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 Topic  
Aug 25 Introduction and motivation HW 0 released
Aug 27 Supervised learning: The setup  
Sep 1 Decision trees HW 0 due, HW1 released
Sep 3 Decision trees (continued)  
Sep 8 Class cancelled due to weather  
Sep 10 Decision trees (continued)  
Sep 15 Linear models  
Sep 17 How good is a learning algorithm? HW1 due, HW2 released
Sep 22 Online learning  
Sep 24 Perceptron  
Sep 29 The Perceptron Mistake Bound Project info due
Oct 1 Least Mean Squares Regression HW2 due, HW3 released
Oct 6 Computational learning theory  
Oct 8 Computational learning theory: PAC learning  
Oct 13 Computational learning theory: Agnostic learning HW3 due, HW4 released
Oct 15 Computational learning theory: VC dimensions  
Oct 20 Mid-semester review Project checkpoint 1
Oct 22 Boosting, ensembles  
Oct 27 Support Vector Machines HW4 due, HW5 released
Oct 29 SVMs (continued)  
Nov 3 Risk minimization  
Nov 5 Bayesian Learning  
Nov 10 Naive Bayes Classification HW5 due, HW6 released
Nov 12 Logistic Regression Project checkpoint 2
Nov 17 Neural Networks  
Nov 19 Neural Networks (continued)  
Nov 24 Nearest Neighbor Classification HW6 due, HW7 released
Nov 26 Thanksgiving! No class.  
Dec 1 Practical advice for building ML applications  
Dec 3 Buffer lecture HW7 due
Dec 8 Final exam (8:00 - 10:00am)  
Dec 11   Project final report due

Additional Topics (which we may cover if time permits)