Index

Homework and Grading

Lectures (Hasegawa-Johnson)

Lectures (Hockenmaier)

Internship Opportunities

Compass2g

Piazza

Videos

1. T Jan 15: Intro to AI
   Reading: Ch. 1
   Slides: pptx, pdf.
   Recommended Problems: 1.1,3,5,7-9,11
2. R Jan 17: History of AI
   Reading: Ch. 1
   Slides: pptx, pdf.
   Recommended Problems: 1.15
3. T Jan 22: Agents Reading: Ch. 2
   Assignment 1 published
   Slides: pptx, pdf.
   Recommended Problems: 2.2-5,10-13
4. R Jan 24: Uninformed Search: Tree Search, including BFS, DFS, IDS
   Reading: Sec. 3.1-3.2
   Slides: pptx, pdf.
   Recommended Problems: 3.2,3,6,8,10,11,16,21,26(a-d)
5. T Jan 29: Informed Search: Greedy, A*
   Reading: Sec. 3.3-3.6
   Slides: pptx, pdf.
   Recommended Problems: 3.14,23,26(e-h),27-29,31
6. R Jan 31: Constraint Satisfaction Problems Reading: Ch. 6
   Slides: pptx, pdf.
   Recommended Problems: 6.2-9,11-14,16
   Assignment 1 due M Feb 4
7. T Feb 5: Planning
   Reading: Sec. 10.1-3
   Slides: pptx, pdf.
   Recommended Problems: 10.1-3, 10.9-11
   Assignment 2 published
8. R Feb 7: Two-Player Games
   Reading: Sec. 5.1-4
   Recommended Problems: 5.3, 5.7, 5.8, 5.13-14
   Slides: pptx, pdf.
9. T Feb 12: Game Theory
   Reading: Sec. 17.5-17.6
   Recommended Problems: 17.17, 17.18, 17.21
   Slides: pptx, pdf.
10. R Feb 14: Probability
    Reading: Ch. 13
    Recommended Problems: 13.1-9, 13.12, 13.15, 16.3
    Slides: pptx, pdf.
11. T Feb 19: Random Variables
    Reading: Ch. 13
    Recommended Problems: 13.10, 13.11, 13.14, 13.18, 13.20
    Slides: pptx, pdf.
    
12. R Feb 21: Stochastic Games, Imperfect Information, and Stochastic Search
    Reading: Sec. 5.5-6
    Recommended Problems: 5.16, 5.20, 5.21
    Slides: pptx, pdf.
    Assignment 2 due M Feb 25
13. T Feb 26: Exam 1 Review
    Review 1, Solutions
14. R Feb 28: Exam 1 (in class)
15. T Mar 5: Bayesian Inference and Bayesian Learning
    Reading: Ch. 13, Recommended Problems: 13.13-24, 18.18, 20.9; repeat 13.22 using Laplace smoothing
    Slides: pptx, pdf.
    Assignment 3 published
16. R Mar 7: Linear Classifiers: Bayesian, Perceptron, Logistic Regression
    Slides: pptx, pdf.
    Reading: Sections 18.6-18.7, Recommended Problems: 18.16,20,21,23,25; 20.4
17. T Mar 12: Linear and PWL Polychotomizers: Softmax, One-Hot Vectors, Cross-Entropy
    Slides: pptx, pdf.
    Reading: Sections 18.6-18.7, Recommended Problems: 18.19,22
18. R Mar 14: Bayesian Networks
    Slides: pptx, pdf.
    Reading: Ch. 14, Recommended Problems: 14.1-8,11-14;16.5,16.17
19. T Mar 26: Bayes Net Inference
    Slides: pptx, pdf.
    Reading: Ch. 20, Recommended Problems: 14.15-16,20.1-3,20.6,20.8,20.10
20. R Mar 28: Hidden Markov Models
    Slides: pptx, pdf.
    Reading: Ch. 15, Recommended Problems: 15.1-6,13-17
    Assignment 3 due M Apr 1
21. T Apr 2: Markov Decision Processes
    Slides: pptx, pdf.
    Reading: Ch. 17, Recommended Problems: 17.1-10
    Assignment 4 published
22. R Apr 4: Reinforcement Learning
    Slides: pptx, pdf.
    Reading: Ch. 21, Recommended Problems: 22.2,4,6,8
23. T Apr 9: Deep Learning
    Slides: pptx, pdf.
24. R Apr 11: Deep Reinforcement Learning
    Slides: pptx, pdf.
25. T Apr 16: Natural Language Processing
    Slides: pptx, pdf.
26. R Apr 18: Speech
    Slides: pptx, pdf.
    Assignment 4 due M Apr 22
27. T Apr 23: Societal Impacts of AI
    Slides: pptx, pdf.
28. R Apr 25: Exam 2 Review
    Review, Solutions.
29. T Apr 30: Exam 2 Review
    Review, Solutions.
30. Exam 2: Monday, May 6, 9:30-10:45am, ECEB 1002, 1013, 1015 and MatSE 100