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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:Jaewook Yoo |
CPSC 311 or equivalent
MWF 9:10am-10:00am, 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 second 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 (no curving). 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'.Attendance is mandatory. Sign-in sheets will be distributed. Faked signatures will be reported to the Aggie Honor System Office. Low attendance will lead to 0% score for class participation.
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://aggiehonor.tamu.edu/
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 | 8/26 | Introduction | Chapter 1 1.1 and 1.2 |
First day of class | slide01
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1 | 8/28 | Introduction | Chapter 26 26.1 and 26.2 |
slide01
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1 | 8/30 | Lisp | Lisp quick ref | slide02
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2 | 9/2 | Lisp, Symbolic Differentiation | Lisp quick ref | Program 1 announced | slide02
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2 | 9/4 | Uninformed Search (BFS,DFS,DLS,IDS) | Chapter 3.1-3.5 (3.6,3.7 optional) | slide03
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2 | 9/6 | Uninformed Search (BFS,DFS,DLS,IDS) | Chapter 3.1-3.5 (3.6,3.7 optional) | slide03
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3 | 9/9 | Informed Search (BestFS,Greedy,A*) | Chapter 4.1-4.3 (4.4 optional)(old 4.1-4.3) | slide03
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3 | 9/11 | IDA*,Heuristic Search, Simulated Annealing, etc. |
Chapter 4 | slide03
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3 | 9/13 | IDA*,Heuristic Search, Simulated Annealing, etc. |
Chapter 4 | slide03
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4 | 9/16 | Game playing Min-Max, Alpha-Beta |
Chapter 5 (optional) and 6.1-6.8 (old 5) | Program 2 announced | Program 1 due 11:59pm | slide03
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4 | 9/18 | Game playing |
Chapter 5 (optional) and 6.1-6.8 (old 5) | slide03
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4 | 9/20 | Game playing wrap up; Representation, logic, frames |
Chapter 5 (optional) and 6.1-6.8 (old 5); Chapter 7.1, 7.3, 7.5, 7.6 (old 6) |
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5 | 9/23 | Propositional Logic | Chapter 7.1, 7.3, 7.5, 7.6 (old 6) | slide04
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5 | 9/25 | Theorem proving | Chapter 9 (old 10) | slide04
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5 | 9/27 | FOL; Theorem proving for FOL |
Chapter 8 (old 7); Chapter 9 (old 10) | Homework 1 announced | slide04
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6 | 9/30 | FOL; Theorem proving for FOL |
Chapter 8 (old 7); Chapter 9 (old 10) | Program 2 due | slide04
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6 | 10/2 | Inference for FOL |
Chapter 9 | slide04
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6 | 10/4 | Inference for FOL |
Chapter 9 | slide04
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7 | 10/7 | Uncertainty | Chapter 13 (old 14) | Homework 1 due | slide05
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7 | 10/9 | Exam #1 | In class | |||
7 | 10/11 | Uncertainty: Probability and decision theory | Chapter 13 (old 14), Chapter 14 (old 15) | slide05
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8 | 10/14 | Uncertainty: Bayes rule | Chapter 13 (old 14), Chapter 14 (old 15) | slide05
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8 | 10/16 | Uncertainty: Probabilistic inference | Chapter 13 (old 14), Chapter 14 (old 15) | slide05
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8 | 10/18 | Uncertainty: Belief network | Chapter 13 (old 14), Chapter 14 (old 15) | slide05
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9 | 10/21 | Neuroevolution | slide06
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9 | 10/23 | Online guest lecture | Watch this video: Juergen Schmidhuber's talk on curiosity and creativity (contents can appear on exam #2) | |||
9 | 10/25 | Online guest lecture | Watch these two videos:(1) IBM Watson: Final Jeoparty! and the Future of Watson. (2) IBM Watson: The Science Behind the Answer (contents can appear on exam #2) | |||
10 | 10/28 | Learning: Inductive learning, Decision trees | Chapter 14 (old 15) | Project proposal due | slideml slide07 |
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10 | 10/30 | Learning: Decision trees, Perceptrons | Chapter 18 | slide07
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10 | 11/1 | Learning: Perceptrons, Multilayer networks | Chapter 20 (old 19) | Homework #2 TBA | slide07
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11 | 11/4 | Learning: Backprop | slide07
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11 | 11/6 | Learning: Backprop | slide07
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11 | 11/8 | Learning: Unsupervised learning, Self-organizing maps | slide07
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12 | 11/11 | Learning: Recurrent networks, Genetic algorithms | slide07
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12 | 11/13 | Problem Posing | Choe and Mann, From problem solving to problem posing. Brain-Mind Magazine, 1:7-8, 2012. [LINK] | slidepp
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12 | 11/15 | Autonomous semantics | see refs in slide08 | Homework #2 due | slidesida
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13 | 11/18 | Autonomous semantics | slidesida
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13 | 11/20 | Planning | slide08
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13 | 11/22 | |||||
14 | 11/25 | Final project presentations | ||||
14 | 11/27 | Final project presentations | ||||
14 | 11/29 | No class (Thanksgiving) | ||||
15 | 12/2 | Final project presentations. |
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).