Schedule

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  
Jan 7 Introduction and motivation HW 0 released
Jan 9 Supervised learning: The setup  
Jan 14 Decision trees HW 0 due, HW1 released
Jan 16 Decision trees (continued)  
Jan 21 Linear models  
Jan 23 Quantifying successful learning  
Jan 28 Online learning HW1 due, HW2 released
Jan 30 Perceptron  
Feb 4 The Perceptron Mistake Bound  
Feb 6 Least Mean Squares Regression  
Feb 11 Computational learning theory: PAC learning HW2 due, HW3 released
Feb 13 Computational learning theory: Consistent learners  
Feb 18 Computational learning theory: Agnostic learning  
Feb 20 Computational learning theory: VC dimension  
Feb 25 Boosting, ensembles HW3 due
Feb 27 Buffer lecture  
Mar 4 Buffer lecture  
Mar 6 Midterm exam (in class)  
Mar 11 Spring break  
Mar 13 Spring break  
Mar 18 Support Vector Machines HW4 released
Mar 20 Risk minimization Project checkpoint 1
Mar 25 Bayesian Learning HW4 due, HW5 released
Mar 27 Naive Bayes Classification  
Apr 1 Logistic Regression  
Apr 3 Neural Networks Project checkpoint 2
Apr 8 Neural Networks (continued) HW5 due, HW6 released
Apr 10 Nearest Neighbor Classification  
Apr 15 Buffer lecture  
Apr 17 Buffer lecture HW6 due
Apr 22 Practical advice for building ML applications Project final report due
Apr 25 Final exam (10:30 am – 12:30 pm)  

Additional Topics (which we may cover if time permits)