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 | Linear models | |
Sep 5 | Online learning | |
Sep 10 | Perceptron | HW 1 due, HW2 released |
Sep 12 | Perceptron (continued) | Project info due |
Sep 17 | Computational learning theory | |
Sep 19 | Computational learning theory (continued) | |
Sep 24 | Computational learning theory: Agnostic learning | HW2 due, HW 3 released |
Sep 26 | Buffer lecture | Project proposal due |
Oct 1 | Buffer lecture | 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: VC dimensions | HW 4 released |
Oct 17 | Boosting, ensembles | |
Oct 22 | Support Vector Machines | |
Oct 24 | SVMs (continued) | |
Oct 29 | Risk minimization | HW4 due, HW 5 released |
Oct 31 | Bayesian Learning | |
Nov 5 | Naive Bayes Classification | Project intermediate report due |
Nov 7 | Naive Bayes (continued) | |
Nov 12 | Logistic Regression | HW 5 due |
Nov 14 | Neural Networks | |
Nov 19 | Neural Networks (continued) | |
Nov 21 | Nearest Neighbor Classification | |
Nov 26 | Buffer lecture | 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 |