Course Information
- Time: Tuesday & Thursday 9:30-10:45 AM
- Location: Olsson Hall 009
- Instructor & Office Hours: Chen-Yu Wei (kfw6en) Thursday 3:30-4:30 PM @ Rice 409
- TAs & Office Hours:
- Matthew Landers (qwp4pk) Monday 4:00-5:00 PM @ Olsson 225
- Haolin Liu (srs8rh) Tuesday 2:00-3:00 PM @ Rice 432
- Xuhui Kang (qhv6ku) Friday 5:00-6:00 PM @ Rice 232
Overview
Artificial Intelligence (AI) is about designing algorithms that enable machines to either behave like humans or assist humans in solving complex problems. In this course, we will explore several fundamental tools that help achieve these goals, including search, logical reasoning, statistical learning, and reinforcement learning. Additionally, we will discuss recent advance in generative AI, along with the associated safety and ethical issues.
Prerequisites
- Data Structures and Algorithms 2 (CS 3100)
- Probability
- Calculus
Grading
- (40%) Assignments: 6 problem sets, each consisting of programming tasks and multiple choice questions.
- (25%) Midterm Exam
- (35%) Final Exam
Late policy for assignments: Seven late days can be used for free. Each additional late day will result in a 10% deduction in the semester’s assignment grade. No assignment can be submitted more than seven days after its deadline.
Platforms
Discussions: Piazza
Homework submissions: Gradescope
Schedule (tentative)
| Date | Topics | Slides and Recommended Reading | Notes |
|---|---|---|---|
| 8/27 | History of AI | Slides, History of AI, Sparks of AGI | |
| 8/29 | Search: BFS, DFS | Slides, Slides(ppt), Sec 3 of AIMA | |
| 9/3 | Search: UCS | HW1 out | |
| 9/5 | Search: Greedy, A* | ||
| 9/10 | Search in Games: α-β pruning | Slides, Slides(ppt), Sec 5 of AIMA | |
| 9/12 | Search in Games: Expectimax, MCTS | ||
| 9/17 | Constraint Satisfaction: Backtracking search, forward checking | Slides, Slides(ppt), Sec 6 of AIMA | HW1 due on 9/16 HW2 out |
| 9/19 | Constraint Satisfaction: Arc consistency, ordering, local search | ||
| 9/24 | Logic: Propositional logic, model checking | Slides, Sec 7 of AIMA | |
| 9/26 | Logic: Forward inference, modus ponens, resolution | ||
| 10/1 | Logic: First-order logic | Sec 8,9 of AIMA | HW3 out HW2 due on 9/30 |
| 10/3 | Probabilistic Models: Probability basics | Slides, Sec 12 of AIMA | |
| 10/8 | Probabilistic Models: Bayesian network, conditional independence, D-separation | Slides, Sec 13 of AIMA | |
| 10/10 | Midterm Review | Slides | HW3-Choices due on 10/9 (late submission is not allowed) |
| 10/15 | Fall Reading Day | ||
| 10/17 | Midterm Exam | ||
| 10/22 | |||
| 10/24 | HW3-Programming due on 10/23 HW4 out |
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| 10/29 | Machine Learning | ||
| 10/31 | |||
| 11/5 | Election Day | ||
| 11/7 | HW4 due on 11/6 HW5 out |
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| 11/12 | |||
| 11/14 | Application: Natural Language Processing (Guest lecture by Yu Meng) |
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| 11/19 | Application: Computer Vision (Guest lecture by Zezhou Cheng) |
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| 11/21 | HW5 due on 11/20 HW6 out |
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| 11/26 | |||
| 11/28 | Thanksgiving | ||
| 12/3 | |||
| 12/5 | HW6 due on 12/4 | ||
| 12/17 | Final Exam |
Books
- [AIMA] Artificial Intelligence: A Modern Approach, 4th Edition, by Stuart Russell and Peter Norvig (3rd Edition)
AI Courses at Other Institutions
- CS188 Introduction to Artificial Intelligence at UC Berkeley
- CS221 Artificial Intelligence: Principles and Techniques at Stanford University
- 6.034 Artificial Intelligence at MIT
Acknowledgement
CYW would like to thank Prof. Yen-Ling Kuo for sharing her experiences and materials for this course. A lot of course materials are taken from Berkeley CS188, Stanford CS221, and MIT 6.034. The programming assignments are taken from Berkeley CS188.