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 9 Introduction and motivation HW 0 released
Jan 11 Supervised learning: The setup  
Jan 16 Decision trees HW 0 due, HW1 released
Jan 18 Decision trees (continued)  
Jan 23 Decision trees (continued)  
Jan 25 Linear models  
Jan 30 Linear models (continued) HW1 due
  Quantifying successful learning  
  Online learning  
Feb 1 Online learning HW2 released
Feb 6 Perceptron  
Feb 8 The Perceptron Mistake Bound  
Feb 13 Least Mean Squares Regression  
Feb 15 Computational learning theory: PAC learning HW2 due, HW3 released
Feb 20 Computational learning theory: Agnostic learning Project info due
Feb 21 Computational learning theory: VC dimension  
Feb 27 Buffer lecture HW3 due
Feb 29 Midterm exam (in class)  
Mar 5 Spring break. No class  
Mar 7 Spring break. No class  
Mar 12 Boosting, ensembles HW4 released
Mar 14 Support Vector Machines Project checkpoint 1
Mar 19 Risk minimization  
Mar 21 Bayesian Learning  
Mar 26 Naive Bayes Classification HW4 due, HW5 released
Mar 28 Logistic Regression  
Apr 2 Neural Networks  
Apr 4 Neural Networks (continued) Project checkpoint 2
Apr 9 Nearest Neighbor Classification HW5 due, HW6 released
Apr 11 Buffer lecture  
Apr 16 Buffer lecture  
Apr 18 Buffer lecture HW6 due
Apr 23 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)