| Class | Date | Due | Topic | Chapters |
| 1 | Tu Jan 17 | Introduction to Machine Learning | 1.1-1.3 | |
| 2 | Th Jan 19 | Supervised Learning | 2 | |
| 3 | Tu Jan 24 | Bayesian Decision Theory | 3 (except Sect. 3.5) | |
| 4 | Th Jan 26 | Parametric Methods | Appendix A, 4 | |
| 5 | Tu Jan 31 | MATLAB session | ||
| 6 | Th Feb 02 | Multivariate Methods | 5 | |
| 7 | Tu Feb 07 | Dimesionality Reduction | 6.1-6.8 | |
| 8 | Th Feb 09 | HW1 & Test1 | Homework 1 discussion & Test 1 | |
| 9 | Tu Feb 14 | Clustering | 7
Sect. 7.7 is optional Tan et al. slides (modified) | |
| 10 | Th Feb 16 | Nonparametric Methods | 8 | |
| 11 | Tu Feb 21 | Decision Trees | 9 | |
| 12 | Th Feb 23 | Catch up & Review for Homework/Test 2 | ||
| 13 | Tu Feb 28 | Design and Analysis of Machine Learning Experiments | 19 | |
| 14 | Th Mar 02 | HW2 & Test2 | Homework 2 discussion & Test 2 | |
| semester break | ||||
| Tu Mar 14 | Class cancelation due to snow | |||
| 15 | Th Mar 16 | Multilayer Perceptrons / Neural Networks | 11 | |
| 16 | Tu Mar 21 | Deep Learning | 11 | |
| 17 | Th Mar 23 | Kernel Machines / Support Vector Machines | 13 | |
| 18 | Tu Mar 28 | Graphical Models / Bayesian Networks | 14 | |
| 19 | Th Mar 30 | Hidden Markov Model I | 15 | |
| 20 | Tu Apr 04 | HW3 & Test 3 | Homework 3 discussion & Test 3 in HL230 Test3 | |
| 21 | Th Apr 06 | Proj Phase I | Project Phase I presentations | |
| 22 | Tu Apr 11 | Hidden Markov Model II | 15 | |
| 23 | Th Apr 13 | Combining Multiple Learners | 17 | |
| 24 | Tu Apr 18 | Reinforcement Learning | 18 | |
| 25 | Th Apr 20 | HW4 & Test4 | Homework 4 discussion & Test 4 | |
| 26 | Tu Apr 25 | Proj Phase II | Project Phase II presentations | |
| 27 | Th Apr 27 | Project Phase II presentations (cont.) | ||
| 28 | Tu May 02 | Project Phase II presentations (cont.) and final remarks | ||