| Class | Date | Due | Topic | Chapters |
| 1 | Fri Aug 28 | Introduction to Machine Learning | 1.1-1.3 | |
| 2 | Tu Sept 01 | Supervised Learning | 2 | |
| 3 | Fri Sept 04 | Bayesian Decision Theory | 3 | |
| 4 | Tu Sept 08 | Parametric Methods I | Appendix A, 4 | |
| 5 | Fri Sept 11 | Parametric Methods II | 4 | |
| 6 | Tu Sept 15 | Multivariate Methods | 5 | |
| 7 | Fri Sept 18 | Dimesionality Reduction I | 6.1-6.8 | |
| 8 | Tu Sept 22 | Dimesionality Reduction II | 6.1-6.8 | |
| 9 | Fri Sept 25 | Clustering | 7
Sect. 7.7 is optional Tan et al. slides (modified) | |
| 10 | Tu Sept 29 | Nonparametric Methods | 8 | |
| 11 | Fri Oct 02 | Decision Trees | 9 | |
| 12 | Tu Oct 06 | Catch up | ||
| 13 | Fri Oct 09 | HW1 & Quiz1 | Homework 1 discussion & Quiz 1 | |
| Sat Oct 10 | Proj1 Phase I | |||
| 14 | Tu Oct 13 | Design and Analysis of Machine Learning Experiments | 19 | |
| 15 | Fri Oct 16 | Proj1 Phase II | Project 1 presentations | |
| semester break | ||||
| 16 | Tu Oct 27 | Multilayer Perceptrons / Neural Networks | 11 | |
| 17 | Fri Oct 30 | Deep Learning | 11 | |
| Sun Nov 01 | Proj2 Phase I Sections A & B | |||
| 18 | Tu Nov 03 | Kernel Machines / Support Vector Machines I | 13 | |
| 19 | Fri Nov 06 | Kernel Machines / Support Vector Machines II | 13 | |
| 20 | Tu Nov 10 | Graphical Models / Bayesian Networks | 14 | |
| 21 | Fri Nov 13 | Hidden Markov Model I | 15 | |
| 22 | Tu Nov 17 | Hidden Markov Model II | 15 | |
| Th Nov 19 | Proj2 Phase I Sections C, D & E | |||
| 23 | Fri Nov 20 | HW2 & Quiz2 | Homework 2 discussion & Quiz 2 | |
| 24 | Tu Dec 01 | Combining Multiple Learners | 17 | |
| 25 | Fri Dec 04 | Reinforcement Learning | 18 | |
| 26 | Tu Dec 08 | HW3 & Exam | Homework 3 discussion & Final Exam | |
| 27 | Fri Dec 11 | Proj2 Phase II | Project 2 presentations | |
| 28 | Tu Dec 15 | Project 2 presentations (cont.) and final remarks | ||
| Fri Dec 18 | Please save this date for a make-up class if needed | |||