| Date | Topic | Required
Readings |
Optional readings |
|
| 1/26 | Overview | Decision Making Under Uncertainty (DMUU) Chapter 1 |
||
| 1/28 | Probabilistic models: Probability |
DMUU 2.1, Murphy 2-2.2, 2.5-2.5.2, 2.8 |
Russell & Norvig Chapter 13, Murphy the rest of 2 |
|
| 2/2 | Probabilistic models: Independence and exact inference | DMUU 2.2-2.2.4, Murphy 10.1, 10.3, 10.5, 20.1-20.3 (without the starred parts) | Russell & Norvig 14 Murphy the rest of 10 and 20 |
assignment due (probability) |
| 2/4 | Probabilistic models: Approximate inference | DMUU 2.2.5, Murphy 20.5, 23.1-23.4, 24.1-24.2 | Murphy the rest of 23 and 24 |
|
| 2/9 | Decision problems: Utility theory | DMUU 3.1 | Russell & Norvig 16 Murphy 5.7 |
assignment due (inference) |
| 2/11 | Decision problems: Decision networks |
DMUU 3.2, Murphy 10.6 |
||
| 2/16 | Sequential problems: MDPs and Dynamic Programming | DMUU 4.1 and 4.2 | Russell & Norvig 17-17.3 | assignment due (decision making) |
| 2/18 | Sequential problems: Structured representations and approximate
dynamic programming |
DMUU 4.3 and 4.5 | ||
| 2/23 | Sequential problems: Online methods and policy search |
DMUU 4.6 and 4.7 | assignment due (MDPs) |
|
| 2/25 | State uncertainty: POMDPs and belief updating |
DMUU 6.1 and 6.2 |
Russell & Norvig 17.4 | |
| 3/1 | State uncertainty: Exact solution methods |
DMUU 6.3 and 6.4 | coding assignment due |
|
| 3/3 | State uncertainty: Other offline and online methods | DMUU 6.5 |
||
| 3/8 | Multi-agent models and applications |
|
assignment due (POMDPs) |
|
| 3/10 | Midterm exam |
|||
| 3/15 | Spring Break! | |||
| 3/17 | Spring Break! | |
||
| 3/22 | Reinforcement learning: The RL problem and model-based methods |
DMUU 5.1-5.3 |
||
| 3/24 | Reinforcement learning: Model free methods |
DMUU 5.4 |
||
| 3/29 | Classification: k-nn and logistic regression |
Murphy 1 and 8-8.3 |
assignment due (RL) |
|
| 3/31 | Logistic and linear regression |
Murphy 7-7.5 (without the starred parts) | ||
| 4/5 | Generative models |
Murphy 3, DMUU 2.3 |
coding assignment due |
|
| 4/7 | More generative models | project topics due |
||
| 4/12 | Decision trees | Murphy 16-16.3 | Russell & Norvig 18.1-18.4 | assignment due (regression) |
| 4/14 | More decision trees | Murphy 16.4 and 16.6 | Russell & Norvig 18.10 | |
| 4/19 | Classification: SVMs |
Murphy 14.5 |
Russell & Norvig 18.9 |
assignment due (nonparametric methods) |
| 4/21 | Mixture models and EM | Murphy 11 |
Russell & Norvig 20.3 | |
| 4/26 | Neural nets | Murphy 16.5 | Russell & Norvig 18.7 | coding assignment due |
| 4/28 | Hidden Markov models | Murphy 17.1-17.5 | Russell & Norvig 15 |
|
| 5/3 | Project presentations |
Will, Tianyi, Alec, Matheus, Connor |
||
| 5/5 | Project presentations |
Madison, Reazul, Bahram, Andreas, Abolfazl, Estuardo |
assignment due (HMMs and NNs) |
|
| 5/9 | Not a class day | AAAI
format |
Final paper due (11:59pm) |