Date | Topic | Required Reading | Notes | Assignments Out | Slides |
---|---|---|---|---|---|
1/12 | Introduction, agents | chaps 1 and 2 | slides | ||
1/17 | State space search, uninformed search | 3.1-3.4 | Project 0, due on Moodle on 1/24, 11:55pm | slides | |
1/19 | Heuristic search, A* | 3.5 | (see previous slides) | ||
1/24 | Finish heuristic search | 3.6 | slides | ||
1/26 | Adversarial search: minimax | 5.1-5.2 | Project 1, due on Moodle on Thu 2/9, 11:55pm Homework 1, due at the start of class on Tue 2/7 |
slides | |
1/31 | Adversarial search: alpha-beta pruning | 5.3 | (see previous slides) | ||
2/2 | Adversarial search: heuristic evaluation functions | 5.4-5.5 | slides | ||
2/7 | Probability I | 13.1-13.3 | (no slides) | ||
2/9 | Probability II | 13.4-13.5 | (no slides) | ||
2/14 | Bayes Nets I | 14.1-14.2 | (no slides) | ||
2/16 | Bayes Nets II (exact inference) | 14.3 | (no slides) | ||
2/21 | Bayes Nets III (approximate inference) | 14.4 | Project 2, due on Moodle on Fri 3/3, 5:00pm |
slides | |
2/23 | Statistical inference: ML & MAP | Homework 2, due at the start of class on Tue 3/14 | slides | ||
2/28 | Statistical inference: Combining evidence | slides | |||
3/2 | Statistical inference: Naive bayes classifiers | slides | |||
3/7 | Spring break | ||||
3/9 | Spring break | ||||
3/14 | Markov chains | all Markov slides | |||
3/16 | Hidden Markov models | ||||
3/21 | Midterm | ||||
3/23 | Hidden Markov models, continued | Project 3, due on Moodle on Thu 4/6, 11:55pm | |||
3/28 | Reinforcement learning I | all RL slides | |||
3/30 | Reinforcement learning II | Homework 3, due at the start of class on Tue 4/11 | |||
4/4 | Reinforcement learning III | ||||
4/6 | Reinforcement learning IV | ||||
4/11 | Reinforcement learning practice | Project 4, due on Moodle on Thu 4/25, 11:55pm | |||
4/13 | Easter break | ||||
4/18 | Neural networks I | all NN slides | |||
4/20 | Neural networks II | ||||
4/25 | Neural networks III | lab | |||
4/27 | Wrapup | wrapup slides |