Instructor: Dana H. Ballard
| Week | Date | Topic | Reading | Assignment | Slides etc |
|---|---|---|---|---|---|
| 0 | 27 Aug | Introduction | Bishop Ch 1 | ||
| 1 | 1 Sep | Probability Theory | Bishop 1 | EM | EMLec-slides CoinPaper |
| 3 Sep | Prob Thy | ||||
| 2 | 8 Sep | Linear Algebra | Classnotes | Eigenfaces | |
| 10 Sep | Dynamical Systems | Classnotes | |||
| 3 | 15 Sep | Information Thy 1 | Bishop 1.6 | DeMixing via ICA | |
| 17 Sep | Information Thy 2 | Classnotes | |||
| 4 | 22 Sep | Optimization Thy | Classnotes | CAR SIMULATION | |
| 24 Sep | Optimization Thy 2 | Bishop Appendix D and E | |||
| 5 | 29 Sep | Perceptrons | Bishop 4.1, 4.2 | ||
| 1 Oct | Backpropagation | Classnotes | |||
| 6 | 6 Oct | SVMs | e.g XOR ; Bishop 325-345 | Neural Nets | |
| 8 Oct | Learning Thy | SVM Tutorial | |||
| 7 | 13 Oct | Decision Trees | Practice Mid Term | ||
| 15 Oct | Review | ||||
| 8 | 20 Oct | MidTerm | MidTerm Answers | ||
| 22 Oct | Bayes Nets Intro | Research Paper | SVM HW | Bayes Net Slides | |
| 9 | 27 Oct | Bayes Net Algorithm | KohonenHW
and Report instructions | ||
| 29 Oct | SOM | KohonenNotes | |||
| 10 | 3 Nov | Markov models | NewNotes | ||
| 5 Nov | Hidden Markov models | ||||
| 11 | 10 Nov | Reinforcement Learning | RL PPT | RL Homework | |
| 12 Nov | RL2 | RL Notes | |||
| 12 | 17 Nov | Genetic Algorithms | GA Notes | Homework: GA | |
| 19 Nov | Genetic Algorithms | ||||
| 13 | 24 Nov | Games | Homework: Games Hauert Paper | ||
| 14 | 1 Dec | Games Evaluations | Zhu:games | ||
| 3 Dec | 2nd Exam | Front , Back , Summary |