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 | Lectures | |
Aug 20 | Introduction and motivation | HW 0 released |
Aug 22 | Supervised learning: The setup | |
Aug 27 | Decision trees | HW 0 due, HW 1 released |
Aug 29 | Decision trees (continued) | |
Sep 3 | Decision trees (continued) | |
Sep 5 | Linear models | |
Sep 10 | Linear models (continued) | HW 1 due, HW2 released |
Sep 12 | Online learning | Project info due |
Sep 17 | Online Learning | |
Sep 19 | Perceptron | |
Sep 24 | Perceptron (continued) | HW2 due, HW 3 released |
Sep 26 | Least Mean Squares Regression | Project proposal due |
Oct 1 | Computational learning theory | HW 3 due |
Oct 3 | Midterm exam (in class) | |
Oct 8 | Fall break. No class. | |
Oct 10 | Fall break. No class. | |
Oct 15 | Computational learning theory: PAC learning | |
Oct 17 | Computational learning theory: Agnostic learning | |
Oct 22 | Computational learning theory: VC dimensions | HW 4 release |
Oct 24 | Boosting, ensembles | |
Oct 29 | Support Vector Machines | |
Oct 31 | SVMs (continued) | |
Nov 5 | Risk minimization | |
Nov 7 | Bayesian Learning | HW4 due, HW 5 released |
Nov 12 | Naive Bayes Classification | Project intermediate report due |
Nov 14 | Logistic Regression | |
Nov 19 | Neural Networks | |
Nov 21 | Neural Networks (continued) | HW 5 due |
Nov 26 | Nearest Neighbor Classification | HW 6 released |
Nov 28 | Thanksgiving! No class. | |
Dec 3 | Buffer lecture | HW 6 due |
Dec 5 | Practical advice for building ML applications | |
Dec 9 | Final exam (8:00 – 10:00 am) | Project final report due |