Day / Time: Wednesday 17:45-20:25pm
Credit Hours: 3
Prerequisite: MCS 5503 or equivalent
Lecture Room: E109, CW21
Lab.: CW21 (for robotics project), CW51
Instructor: Chan-Jin Chung, Ph.D.

• Office Room: Science 112
• Phone: (248) 204-3504
• Fax: (248) 204-3518 (this fax number is for the whole building and senders should be sure to place instructor's name on the fax)
• Dept. Secretary: (248) 204-3560
• Math/CS Drop Box: in front of S120 door.
• Email: CHUNG@ltu.edu
• Black Board for the class
• LTU webpage at http://www3.ltu.edu/~chung (syllabus, announcements, lecture notes, etc.)
• Office Hours: check out http://www3.ltu.edu/~chung/s01sch.htm

• Title: Artificial Intelligence, 3/e, 1992, 750 pp., ISBN 0-201-53377-4
• Author: Patrick Henry Winston, Massachusetts Institute of Technology, 
• Features
  • Semiformal representation and procedure specifications bring the ideas to within a step or two of implementation and highlight unifying themes.
  • Application examples provide a glimpse of the ideas at work in real-world systems.
  • Powerful ideas and principles are identified and emphasized.

Internet Resources

• the American Association for Artificial Intelligence (AAAI)
• http://ai.about.com/compute/ai/
• Many more...

Course Objectives

• Introduction to advanced topics in Artificial Intelligence and Adaptability theories and techniques (50%)
• Applying advanced theories and techniques to the development of practical Intelligent Systems as class projects (50%)
• To plan MSCS capstone project I and II

• Review: Search and Optimization methods, Trees and Adversarial Search, Version Space, Artificial Neural Nets, and Evolutionary Computation

• Constrained Search and Constraint satisfaction (Chap 11, 12 +)

• Knowledge representation: Representation of space, time, events, and actions (Chap2, 9, 10, +)

• Reasoning: predicate calculus and resolution, rules and rule chaining, logic, probabilistic reasoning, Bayes' theorem, reasoning with uncertainty - Fuzzy logic. (chap 7, 8, 13, +)

• Learning by Analyzing Differences,  Correcting Mistakes, Recording Cases (decision tree learning, building Identification Trees, Training Approximation Nets: Chap 16, 18, 19, 21, 24)

• More on Artificial Neural Nets and applications. (Chap 22)

• Neuro-Fuzzy Systems

• Learning by Simulating Evolution. (Chap 25) - Evolution Strategies, Evolutionary Programming, Genetics Algorithms, Genetic Programming, and Cultural Algorithms

• Introduction to visual perception (computer vision) and natural language understanding. (Chap 26, 27, 28)
• Autonomous Robotics and Education
• Data Mining
• Personalization and Adaptable Websites

Problems to solve as home works or Projects (tentative): 

• Implementation of Fuzzy Controller, Neuro Fuzzy Systems, Hopfield Network and Kohonen's Network
• Constrained non linear function optimization
• Multi objective function optimization
• Combinatorial Optimization problems such as Gear ratio, Boole problem, Bean Packing, TSP, 0/1 Knapsack, job-shop scheduling problems (*)
• 4x4 tic-tac-toe game
• Robotics (Lego or Khepera) (*)
• 2D and 3D design optimization (*) - cec2001.kaist.ac.kr/ (click competitions)
• Intelligent Internet Applications (*)
• Intelligent Web devices using Java TINI system
• Your own project, which should be approved by the instructor

(*) Required Projects

Tentative Schedule 

Class Format and Grading: Total 200 points

• Homework assignments - 10 points
• Seminar - 20 points
• Projects including demos: 120
• 1 final (everything covered in class) - 50 points

This score will be translated into a letter grade based upon the percentages given below.

Class Policies

• Attendance is essential to doing well in the course. The exams will focus primarily (but not exclusively) on material presented in the lectures.
• If you are unable to attend a meeting, it is your responsibility to obtain the material from other students or instructor.

Exam Policies

• There will be no makeup exams will be given.
• Closed books, closed notes, and closed neighbors.

Seminar Policies

• Topics and due date will be given by the instructor
• Late penalty is 30%.

• Homework assignments / Projects must be done individually.
• Late submission penalty is 30%.
• No submission/demo will be accepted beyond a week after the due date.

Policy on Academic Misconduct

• Plagiarism is a serious academic offense. DO NOT COPY THE WORK OF OTHERS. Failure to observe this will result in zero point for the assignment and will be handled in accordance with University Policy.
• Cheating during exams is also a very serious academic offense and will be handled in accordance with University Policy.