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CS 188 Summer 2023
Syllabus
| Wk. | Date | Lecture (pptx files) |
Readings (AIMA, 4th ed.) |
Discussion | Homework | Project |
|---|---|---|---|---|---|---|
| 1 | Tue Jun 20 | 1. Intro, Overview of AI, Rational Agents, Utilities and Lotteries Slides | Ch. 1, 2 Note 1 | 1. Tower of Hanoi, Search Review Worksheet / Solutions |
Project 0 tutorial
(due Friday, June 23) | |
| Wed Jun 21 | 2. Uninformed Search, Informed search with Heuristics, Tree Search vs. Graph Search Slides | Ch. 3 Note 2 | 2. Informed Search, Heuristics Worksheet / Solutions |
HW1
(due Fri, Jun 23) Gradescope | Project 1 search
(due Tuesday, June 27) | |
| Thu Jun 22 | 3. Constraint Satisfaction, Forward Checking and Recursive Backtracking, Arc Consistency Slides | 6.1 - 6.3 Note 3 | 3. IDA*, Search Challenge Problems Worksheet / Solutions |
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| 2 | Mon Jun 26 | 4. Finish up CSPs, Local Search, Hill-Climbing and Simulated Annealing Slides | 6.4 - 6.5, 4.1 - 4.2 Note 4 | 4. CSPs, Local Search Worksheet / Solutions |
HW2
(due Fri, Jun 30) Gradescope | |
| Tue Jun 27 | 5. Game Trees, Minimax, Alpha-beta pruning, Expectimax Slides | 5.1 - 5.3 Note 5 | 5. CSP Air Traffic, Game Trees Worksheet / Solutions |
Project 2
(due Wednesday, July 5) | ||
| Wed Jun 28 | 6. Alpha-Beta Pruning, Expectimax, Monte Carlo Tree Search Slides | 5.4 - 5.5 Note 6 | 6. Pruning Practice, Challenge Problems Worksheet / Solutions |
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| Thu Jun 29 | 7. Conditional Probability Review, Bayes Nets, Inference by Enumeration Slides | 14.1 - 14.2 Note 7 | 7. Probability Review, Simple Bayes Net Worksheet / Solutions |
Project 3
(due Tuesday, July 11) | ||
| 3 | Mon Jul 03 | 8. No class | 8. Variable Elimination, Representation Worksheet / Solutions |
HW3
(due Fri, July 7) Gradescope | ||
| Tue Jul 04 | 9. No class, Independence Day | No Discussion | ||||
| Wed Jul 05 | 10. D-Separation, Structure of Bayes Nets, Approximate inference with sampling | 14.4 | 9. D-Separation, Challenge Questions Worksheet / Solutions |
Project 4
(due Thursday, July 20) | ||
| Thu Jul 06 | 11. Bayes Net Sampling, Markov Chain Monte Carlo | 14.5 | 10. Sampling in Bayes Nets, Review Worksheet / Solutions |
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| 4 | Mon Jul 10 | 12. Markov Chains Review, Mini-Forward Algorithm, Stationarity, HMMs, Forward Algorithm Slides | 15.1 - 15.6 Note 12 | 11. Markov Models, Forward Algorithm Worksheet / Solutions |
HW4
(due Fri, July 14) Gradescope | |
| Tue Jul 11 | 13. HMMs, Forward Algorithm, Viterbi Algorithm, Particle Filtering | 15.1 - 15.6 | 12. Viterbi Algorithm, Particle Filtering Worksheet / Solutions |
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| Wed Jul 12 | 14. Finish HMMs, Rational Preferences and Decision Networks Slides | 16.1 - 16.3 | 13. HMM Challenge Questions Worksheet / Solutions |
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| Thu Jul 13 | 15. Decision Networks and VPI, Midterm Review Slides | 16.5 - 16.7 Note 15 | Midterm Review Sections | Project 5
(due Thursday, July 27) | ||
| 5 | Mon Jul 17 | 16. No Lecture: Midterm 7-9pm in Wheeler 150 | 17.1 - 17.3 | 14. Decision Networks, VPI Worksheet / Solutions |
HW5
(due Mon, July 24) Gradescope | |
| Tue Jul 18 | 17. MDPs, Policies, Values, Q-values, Dynamic Programming Slides | 17.1 - 17.3 Note 17 | 15. MDPs, Dynamic Programming Worksheet / Solutions |
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| Wed Jul 19 | 18. Intro to Reinforcement Learning, Bandit Problems, Regret Slides | 22.1 - 22.6 Note 18 | 16. MDP Challenge Questions Worksheet / Solutions |
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| Thu Jul 20 | 19. RL: Temporal Difference Learning, Q-learning | 22.1 - 22.6 | 17. Reinforcement Learning Worksheet / Solutions |
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| 6 | Mon Jul 24 | 20. Machine Learning: Learning from Data, Training/Validation/Test, MLE and MAP, Naive Bayes Slides | 20.1 - 20.2, 18.8 Note 20 | 18. Reinforcement Learning Part 2 Worksheet / Solutions |
HW6
(due Fri, July 28) Gradescope | Project 6
(due Saturday, August 5) |
| Tue Jul 25 | 21. Machine Learning: Features, Linear Regression, Regularization and Ridge Regression | 18.6 | 19. ML Potpourri, MLE, Naive Bayes Worksheet / Solutions |
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| Wed Jul 26 | 22. Optimization and Gradient Methods, Perceptron, Logistic Regression, MLP, Loss Functions Slides | 21.1 - 21.5 | 20. Vector Calculus Review, Linear Regression Worksheet / Solutions |
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| Thu Jul 27 | 23. Neural Networks: Forward pass, Representationability Slides | 21.1 - 21.5 | 21. Perceptron, Logistic Regression Optimization Worksheet / Solutions |
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| 7 | Mon Jul 31 | 24. Optimization in Neural Networks: Backpropagation, Autodiff, Training Slides | 21.1 - 21.5 Note 24 | 22. Neural Network Representations Worksheet / Solutions |
HW7
(due Fri, Aug 4) Gradescope | |
| Tue Aug 01 | 25. Learning Theory, Decision Trees, and Additional Optimization Methods Slides | 21.1 - 21.5 | 23. Neural Networks and Optimization Worksheet / Solutions |
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| Wed Aug 02 | 26. Lecture Cancelled | 24. Backpropagation in Neural Networks Worksheet / Solutions |
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| Thu Aug 03 | 27. Advanced Topics: NLP Slides | 25. Final Review Sections Search/CSPs: Worksheet / Solutions |
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| 8 | Mon Aug 07 | 28. Advanced Topics: Computer Vision Slides | ||||
| Tue Aug 08 | 29. Advanced Topics: Reinforcement Learning Slides | |||||
| Wed Aug 09 | 30. Final Review Slides | |||||
| Thu Aug 10 | 31. Final Exam (VLSB 2050) |