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 |