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 Linear models (continued)  
  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 Class cancelled  
Feb 18 COLT: Consistent learners  
Feb 20 COLT: Learnability results  
Feb 25 COLT: Agnostic learning HW3 due
Feb 27 COLT: VC dimension  
Mar 4 Mid-semester review  
Mar 6 Midterm exam (in class)  
Mar 11 Spring break  
Mar 13 Spring break  
Mar 18 Boosting, ensembles  
Mar 20 Support Vector Machines HW4 released, Project checkpoint 1
Mar 25 SVMs (continued)  
Mar 27 Tutorial: Practical ML with sklearn  
Apr 1 Stochastic gradient descent for SVMs  
Apr 3 Risk minimization HW 4 due, HW5 released
  Bayesian Learning  
Apr 8 Bayesian learning (continued) Project checkpoint 2
  Logistic Regression  
Apr 10 Neural Networks  
Apr 15 Neural Networks (continued)  
Apr 17 Nearest Neighbor Classification  
Apr 22 Practical advice for building ML applications HW5, project final report due
Apr 25 Final exam (10:30 am – 12:30 pm)  

Additional Topics (which we may cover if time permits)