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General Information | Resources | Weekly Schedule | Credits | Lecture Notes | Example Code | Read-Only Board |
I. General Information |
Instructor:Dr. Yoonsuck Choe |
TA:Huei-Fang Yang |
CPSC 311 or equivalent
Tue/Thu 2:20pm-3:35pm, HRBB 113
To understand the problems in AI and to learn how to solve them:
- traditional methods in AI (search, pattern matching, logical inference, theorem proving, etc.).
- modern approaches in AI (learning, probabilistic approaches, etc.).
Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (AIMA, hereafter), 3rd Edition, Prentice Hall, New Jersey, 2010.
Book Homepage
* The first edition may be okay if that's what you have.
See the Weekly Schedule section for more details.
- Introduction
- LISP
- Search
- Game playing, alpha-beta pruning
- Propositional Logic, first-order logic, theorem proving
- Uncertainty, probabilistic approaches
- Learning
- Special topics
Grading will be on the absolute scale. The cutoff for an `A' will be 90% of total score, 80% for a `B', 70% for a `C', 60% for a `D', and below 60% for an 'F'.If you are absent without any prior notification to the instructor, your class participation score will be set to 0% at the very first occurrence, except for excuses allowed by the university rules (medical, etc.).
AGGIE HONOR CODE: An Aggie does not lie, cheat, or steal or tolerate those who do.Upon accepting admission to Texas A&M University, a student immediately assumes a commitment to uphold the Honor Code, to accept responsibility for learning, and to follow the philosophy and rules of the Honor System. Students will be required to state their commitment on examinations, research papers, and other academic work. Ignorance of the rules does not exclude any member of the TAMU community from the requirements or the processes of the Honor System.
For additional information please visit: http://www.tamu.edu/aggiehonor/
Local Course Policy:
- All work should be done individually and on your own unless otherwise allowed by the instructor.
- Discussion is only allowed immediately before, during, or immediately after the class, or during the instructor's office hours.
- If you find solutions to homeworks or programming assignments on the web (or in a book, etc.), you may (or may not) use it. Please check with the instructor.
The Americans with Disabilities Act (ADA) is a federal anti-discrimination statute that provides comprehensive civil rights protection for persons with disabilities. Among other things, this legislation requires that all students with disabilities be guaranteed a learning environment that provides for reasonable accommodation of their disabilities. If you believe you have a disability requiring an accommodation, please contact the Department of Student Life, Services for Students with Disabilities, in Cain Hall or call 845-1637.
II. Resources |
III. Weekly Schedule and Class Notes |
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1 | 8/31 | Introduction | Chapter 1 1.1 and 1.2 |
First day of class | slide01.pdf |
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1 | 9/2 | Introduction, Lisp | Chapter 26 26.1 and 26.2 |
slide01.pdf slide02.pdf |
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2 | 9/7 | Lisp, Symbolic Differentiation | Lisp quick ref | Program 1 announced | slide02.pdf |
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2 | 9/9 | Uninformed Search (BFS,DFS,DLS,IDS) | Chapter 3.1-3.5 (3.6,3.7 optional) | slide03.pdf |
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3 | 9/14 | Informed Search (BestFS,Greedy,A*) | Chapter 4.1-4.3 (4.4 optional)(old 4.1-4.3) | slide03.pdf |
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3 | 9/16 | IDA*,Heuristic Search, Simulated Annealing, etc. |
Chapter 4 | slide03.pdf |
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4 | 9/21 | Game playing Min-Max, Alpha-Beta |
Chapter 5 (optional) and 6.1-6.8 (old 5) | Program 2 announced | Program 1 due (11:59am) | slide03.pdf |
4 | 9/23 | Game playing |
Chapter 5 (optional) and 6.1-6.8 (old 5) | slide03.pdf |
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5 | 9/28 | Game playing wrap up; Propositional Logic | Chapter 7.1, 7.3, 7.5, 7.6 (old 6) | slide03.pdf slide04.pdf |
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5 | 9/30 | Theorem proving | Chapter 9 (old 10) | Homework 1 announced | slide04.pdf |
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6 | 10/5 | FOL; Theorem proving for FOL |
Chapter 8 (old 7); Chapter 9 (old 10) | slide04.pdf |
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6 | 10/7 | Inference for FOL |
Chapter 9 | Program 2 due (11:59pm) | slide04.pdf |
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7 | 10/12 | Uncertainty | Chapter 13 (old 14) | Homework 1 due, in class | slide05.pdf |
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7 | 10/14 | Exam #1 | In class | |||
8 | 10/19 | Uncertainty | Chapter 13 (old 14), Chapter 14 (old 15) | slide05.pdf |
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8 | 10/21 | Neuroevolution | Plus final project discussion | slide06.pdf |
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9 | 10/26 | Guest lecture | Ji Ryang Chung, Emergence of Memory: An Alife Simulation | |||
9 | 10/28 | Learning | Chapter 14 (old 15) | slide07.pdf |
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10 | 11/2 | Advanced topic, Learning | Chapter 18, see refs in slide08 | slide07.pdf slide08.pdf |
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10 | 11/4 | Guest lecture | Dr. Ronnie Ward | |||
11 | 11/9 | Learning | Chapter 18 | slide07.pdf |
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11 | 11/11 | Learning | Chapter 20 (old 19) | Homework 2 announced | slide07.pdf |
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12 | 11/16 | Guest lecture | Tim Mann, reinforcement learning and skill transfer | |||
12 | 11/18 | Learning | slide07.pdf |
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13 | 11/23 | Planning | Homework 2 due, in class | slide09.pdf |
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13 | 11/25 | No class (Thanksgiving) | ||||
14 | 11/30 | Exam #2 | ||||
14 | 12/2 | Project presentation | ||||
15 | 12/7 | Project presentation |
IV. Credits |
Many ideas and example codes were borrowed from Gordon Novak's AI Course and Risto Miikkulainen's AI Course at the University of Texas at Austin (Course number CS381K).