|
|
|
General Information | Resources | Weekly Schedule | Credits | Lecture Notes | Example Code | Read-Only Board |
I. General Information |
Instructor:Dr. Yoonsuck Choe |
TA:Randall Reams |
Grader:Anavil Tripathi |
CPSC 311 or equivalent
Tue/Thu 3:55pm-5:10pm, ETB 1020
To understand the problems in AI and to learn how to solve them:
- traditional methods in AI (search, pattern matching, logical inference, theorem proving, planning, 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
See the Weekly Schedule section for more details.
- Introduction
- LISP
- Search
- Game playing, alpha-beta pruning
- Propositional Logic, first-order logic, theorem proving
- Planning
- Uncertainty, probabilistic approaches
- Learning
- Advanced topics
Make up exams:
- There will be no make up exam for those who do not show up for the exam without 24 hour prior notice that is due to legitimate reasons.
- For illness-related absence, explicit doctor's note of excuse (e.g. "<Full Name> is unable to attend classes on <Date> due to illness.") is required. Just a note acknowledging that you visited the doctor's clinic or student health center is not enough.
- Make up exams will be different from the original exams although the difficuly will be adjusted to be comparable.
There will be 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 sheet will be distributed on random dates (about 10 times). More than 2 recorded absences will lead to 0% for attendance. Faked signatures will get 0% for attendance and an additional 15% penalty toward the final grade.
Late penalty: 1 point (out of 100) per hour. Late submissions will not be accepted 4 days after the deadline and/or after the solution has been posted.
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, or call 845-1637.
II. Resources |
III. Weekly Schedule and Class Notes |
|
|
|||||
1 | 8/30 | Introduction | Chapter 1 | First day of class | slide01
|
|
1 | 9/1 | Introduction, LISP |
Chapter 26 26.1 and 26.2 Lisp quick ref |
slide01 slide02 |
||
2 | 9/6 | Symbolic Differentiation | Lisp quick ref | slide02
|
||
2 | 9/8 | Uninformed Search (BFS,DFS,DLS,IDS) | Chapter 3.1-3.6 | slide03
|
||
3 | 9/13 | Informed Search (BestFS,Greedy,A*) | Chapter 4.1-4.3 (4.4 optional) | Homework 1 announced. See eCampus. |
slide03
|
|
3 | 9/15 | IDA*,Heuristic Search, Simulated Annealing, Constraint Satisfaction, etc. |
Chapter 4, Chapter 6.1 | Program 1 announced | slide03
|
|
4 | 9/20 | Game playing Min-Max, Alpha-Beta |
Chapter 5 | slide03
|
||
4 | 9/22 | Game playing wrap up; Representation, logic, frames |
Chapter 5 Chapter 7 |
Homework 1 due 9/25 Sunday 11:59pm |
slide03 slide04 |
|
5 | 9/27 | Guest Lecture by Dr. Thomas Ioerger | Intelligent Agents. Lecture slides | |||
5 | 9/29 | Guest Lecture by Jaewook Yoo | Evolving Tool Use Behavior | |||
6 | 10/4 | Propositional Logic | Chapter 7 | Homework 2 TBA | slide04
|
|
6 | 10/6 | Theorem proving First order logic (FOL) |
Chapter 8; Chapter 9 | Program 1 due | slide04
|
|
7 | 10/11 | Midterm Exam | In class | Notice: Undefined offset: 4 in /home/faculty/choe/web_project/625-16fall/index.php on line 416 | ||
7 | 10/13 | Theorem proving for FOL |
Chapter 8; Chapter 9 | slide04
|
||
8 | 10/18 | Inference for FOL |
Chapter 9 | Program 2 announced | slide04
|
|
8 | 10/20 | Uncertainty | Chapter 13 | Homework 2 due | slide05
|
|
9 | 10/25 | Uncertainty: Decision theory, Bayes rule |
Chapter 13, Chapter 14 | slide05
|
||
9 | 10/27 | Uncertainty: Belief network | Chapter 13, Chapter 14 | slide05
|
||
10 | 11/1 | Planning, Machine Learning Intro | Chapter 7.2, 7.7, 10.4.2, 11 | slide-planning slideml3 |
||
10 | 11/3 | Advanced topic: Neuroevolution | slide06
|
|||
11 | 11/8 | Learning: Inductive learning, Decision trees, Perceptrons | Chapter 14, Chapter 18 | slide07
|
||
11 | 11/10 | Learning: Perceptrons, Multilayer networks | Chapter 18, Chapter 20 | slide07
|
||
12 | 11/15 | Guest Lecture by Dr. Ruihong Huang | Natural Language Processing Case Study: IBM Watson | slide-nlp
|
||
12 | 11/17 | Learning: Backpropagation | Chapter 18, Chapter 20 | Program 2 due | slide07
|
|
13 | 11/22 | Learning: Unsupervised learning, Self-organizing maps | Chapter 18, Chapter 20 | Combined Homework 3 / Program 3 announced | slide07
|
|
13 | 11/24 | Thanksgiving: No class | ||||
14 | 11/29 | Learning: Recurrent networks, Genetic algorithms | Chapter 18, Chapter 20 | slide07
|
||
14 | 12/1 | Advanced topic: Deep learning | slide-dl
|
|||
15 | 12/6 | Advanced topic: Problem Posing | Full slides with animations [pdf] | slide-probposing
|
||
16 | 12/13 | Final exam: December 13 (Tuesday): 1-3pm, in ETB 1020 | Homework 3/Program 3 due 12/11 11:59pm |
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).