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CS 188 Summer 2024
Announcements
Calendar
| Wk. | Date | Lecture (pptx files) |
Readings (AIMA, 4th ed.) |
Discussion (future is tentative) |
Homework | Project |
|---|---|---|---|---|---|---|
| 1 | Mon Jun 17 | 1. Intro, Overview of AI, Rational Agents, Utilities and Lotteries (Evgeny) Slides |
Ch. 1, 2 Note 1 | No discussion | HW0 [optional]
(due N/A) Part A | Project 0 [optional]
(due Fri, Jun 21) |
| Tue Jun 18 | 2. Uninformed Search, Informed search with Heuristics, Tree Search vs. Graph Search (Evgeny) Slides |
Ch. 3 Note 2 | HW1
(due Fri, Jun 21) Part A Part B Solutions | Project 1
(due Tue, Jun 25) | ||
| Wed Jun 19 | No class: Juneteenth |
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| Thu Jun 20 | 3. Constraint Satisfaction, Forward Checking and Recursive Backtracking, Arc Consistency (Eve) Slides |
6.1 - 6.3 Note 3 | 1. Uninformed Search Worksheet / Solutions Exam Prep / Solutions |
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| 2 | Mon Jun 24 | 4. Finish up CSPs, Local Search, Hill-Climbing and Simulated Annealing (Eve) Slides |
6.4 - 6.5, 4.1 - 4.2 Note 4 | 2. Informed Search Worksheet / Solutions Exam Prep / Solutions |
HW2
(due Fri, Jun 28) Part A Part B | |
| Tue Jun 25 | 5. Game Trees, Minimax, Alpha-beta pruning, Expectimax (Evgeny) Slides |
5.1 - 5.3 Note 5 | ||||
| Wed Jun 26 | 6. Alpha-Beta Pruning, Expectimax, Monte Carlo Tree Search (Evgeny) Slides |
5.4 - 5.5 Note 6 | 3. CSPs Worksheet / Solutions Exam Prep / Solutions |
Project 2
(due Tue, Jul 2) | ||
| Thu Jun 27 | 7. Conditional Probability Review, Bayes Nets, Inference by Enumeration (Evgeny) Slides |
14.1 - 14.2 Note 7 | ||||
| 3 | Mon Jul 01 | 8. D-Separation, Structure of Bayes Nets, Approximate inference with sampling (Evgeny) Slides |
14.4 | 4. Games Worksheet / Solutions Exam Prep / Solutions |
HW3
(due Mon, Jul 8) Part A Part B | |
| Tue Jul 02 | 9. Bayes Net Sampling, Markov Chain Monte Carlo (Evgeny) Slides |
14.5 | ||||
| Wed Jul 03 | No class |
5. Probability, Bayes Nets, Variable Elimination Worksheet / Solutions / Video Exam Prep / Solutions / Video |
Project 3
(due Wed, Jul 17) | |||
| Thu Jul 04 | No class: Independence Day |
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| 4 | Mon Jul 08 | 10. Markov Chains Review, Mini-Forward Algorithm, Stationarity, HMMs, Forward Algorithm (Eve) Slides |
15.1 - 15.6 Note 10 | 6. Midterm Review Search: Worksheet / Solutions |
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| Tue Jul 09 | 11. HMMs, Forward Algorithm, Viterbi Algorithm, Particle Filtering (Eve) Slides |
15.1 - 15.6 | HW4
(due Mon, Jul 15) Part A Part B | |||
| Wed Jul 10 | 12. Finish HMMs, Midterm Review (Evgeny) Slides |
16.1 - 16.3 | 7. D-Separation, HMMs Worksheet / Solutions Exam Prep / Solutions |
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| Thu Jul 11 | Midterm 2-4 PM PT |
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| 5 | Mon Jul 15 | 13. Rational Preferences, Decision Networks and VPI, MDPs, Policies (Evgeny) Slides |
16.5 - 16.7 Note 13 | 8. Particle Filtering, Decision Networks Worksheet / Solutions Exam Prep / Solutions |
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| Tue Jul 16 | 14. Values, Q-values, Policy Iteration, Dynamic Programming, RL Bandit Problems (Evgeny) Slides |
17.1 - 17.3 Note 14 | HW5
(due Fri, Jul 19) Part A Part B | |||
| Wed Jul 17 | 15. Reinforcement Learning, Regret, Temporal Difference Learning, Q-learning, Approximate Q-learning (Evgeny) Slides |
22.1 - 22.6 Note 15 | 9. MDPs, VPI Worksheet / Solutions Exam Prep / Solutions |
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| Thu Jul 18 | 16. RL: Policy Search, Actor-Critic, DDQN; Least Squares, Gradients (Evgeny) Slides |
22.1 - 22.6 | Project 4
(due Wed, Jul 24) | |||
| 6 | Mon Jul 22 | 17. Machine Learning: Learning from Data, Training/Validation/Test, MLE and MAP, Naive Bayes (Eve) Slides |
20.1 - 20.2, 18.8 Note 17 | 10. RL Worksheet / Solutions Exam Prep / Solutions |
HW6
(due Fri, Jul 26) Part A Part B | |
| Tue Jul 23 | 18. Machine Learning: Features, Linear Regression, Regularization and Ridge Regression (Eve) Slides |
18.6 | ||||
| Wed Jul 24 | 19. Optimization and Gradient Methods, Perceptron, Logistic Regression, MLP, Loss Functions (Eve) Slides |
21.1 - 21.5 | 11. MLE, Naive Bayes, Regressions Worksheet / Solutions Exam Prep / Solutions |
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| Thu Jul 25 | 20. Neural Networks: Forward pass, Representationability (Eve) Slides |
21.1 - 21.5 | Project 5
(due Fri, Aug 2) | |||
| 7 | Mon Jul 29 | 21. Optimization in Neural Networks: Backpropagation, Autodiff, Training (Eve) Slides |
21.1 - 21.5 Note 21 | 12. Neural Networks, Loss Functions Worksheet / Solutions Exam Prep / Solutions |
HW7
(due Fri, Aug 2) Part A Part B | |
| Tue Jul 30 | 22. Learning Theory, Decision Trees, and Additional Optimization Methods (Eve) Slides |
21.1 - 21.5 | ||||
| Wed Jul 31 | 23. Advanced Topics: NLP (Eve) Slides |
13. Vector Calculus, Backpropogation Worksheet / Solutions Exam Prep / Solutions |
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| Thu Aug 01 | 24. Advanced Topics: AI Ethics, Fairness, and Safety (Eve) Slides |
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| 8 | Mon Aug 05 | 25. Advanced Topics: Guest Lecture (Marwa Abdulhai) Slides |
14. Final Review Search/CSP: Worksheet / Solutions |
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| Tue Aug 06 | No class |
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| Wed Aug 07 | 26. Final Review (Eve) Slides |
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| Thu Aug 08 | Final 2-5 PM PT |