Date | Topic | Required Reading | Notes | Assignments Out | Slides |
---|---|---|---|---|---|
8/23 | Introduction, agents | chaps 1 and 2 | slides | ||
8/28 | State space search: uninformed search | 3.1-3.4 | Project 0, due on Moodle on 9/5, 11:55pm | slides | |
8/30 | State-space search: more uninformed search | 3.5 | slides | ||
9/4 | State-space search: heuristic search, A* algorithm | 3.6 | |||
9/6 | Adversarial search: minimax algorithm | 5.1, 5.2 | Project 1, due on Moodle on Thu 9/20, 11:55pm Homework 1, due at the start of class on Thu 9/13 |
slides | |
9/11 | Adversarial search: alpha-beta pruning | 5.3 | slides | ||
9/13 | Adversarial search: heuristics evaluation functions | 5.4, 5.5 | slides | ||
9/18 | Probability I | 13.1-13.3 | (no slides) | ||
9/20 | Probability II | 13.4-13.5 | (no slides) | ||
9/25 | Bayes nets I | 14.1-14.2 | (no slides) | ||
9/27 | Bayes nets II (exact inference) | 14.3 | Project 2, due on Moodle on Thu 10/11, 11:55pm Homework 2, due at the start of class on Tue 10/9 |
(no slides) | |
10/2 | Bayes nets III (approximate inference) | 14.4 | slides | ||
10/4 | Statistical inference: ML & MAP | slides | |||
10/9 | Statistical inference: Combining evidence | slides | |||
10/11 | Statistical inference: Naive bayes classifiers | slides | |||
10/16 | Fall break | ||||
10/18 | Markov chains | all Markov slides | |||
10/23 | Midterm | ||||
10/25 | Hidden Markov models I | (see above) | |||
10/30 | Hidden Markov models II | Project 3, due on Moodle on Tue 11/13, 11:55pm | (see above) | ||
11/1 | Reinforcement learning | ||||
11/6 | Reinforcement learning II | ||||
11/8 | Reinforcement learning III | ||||
11/13 | Reinforcement learning IV | Homework 3, due at the start of class on 11/20 | all RL slides | ||
11/15 | Neural networks introduction | Project 4, due on Moodle on Tue 12/4, 11:55pm | |||
11/20 | Neural networks I | all NN slides | |||
11/22 | Thanksgiving | ||||
11/27 | Neural networks II | ||||
11/29 | Neural networks III | ||||
12/4 | Wrapup | slides |