Day / Time: Wednesday 17:45-20:25pm
Credit Hours: 3
Prerequisite: MCS 2534 (Data Structures) and (C++ or Java)
Lecture Room: S204
Lab.: CW21 (if you choose robotics project)
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
• LTU webpage at http://www3.ltu.edu/~chung (syllabus, announcements, lecture notes, etc.)
• Office Hours: Wed 8:25 - 8:45pm or by appointment

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

Part I: representing knowledge and reasoning methods 

• Fundamental issues in Intelligent Systems. (Chap 1, +)

• Search and Optimization methods

Generate and Test, Means-End Analysis, and Problem Reduction. (Chap 3)
Nets and Basic Search, and Optimal Search. (Chap 4, 5)
Trees and Adversarial Search. (Chap 6)

Constrained Search, 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, +)

Part II: learning 

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

• Learning by Managing Multiple Models. (Version Space) (Chap 20, will be covered early)

• Learning by Training Neural Nets. (Chap 22)

• Learning by Training Perceptrons. (Chap 23)

• Learning by Simulating Evolution. (Chap 25, will be covered early)

Part III: visual perception (computer vision) and natural language understanding. (May not be covered this semester)

Part IV: Applications of Intelligent Systems - See class projects below.

Problems to solve as home works: The price is right game, non linear function optimization, Boole problem, scheduling problems, TSP, 4x4 tic-tac-toe, etc.

Tentative Schedule 

Class Format and Grading: Total 200 points

• Homework assignments - 50 points
• 1 midterm: 50 points 
• 1 project including demo: 40
• 1 final (everything covered in class) - 60 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.

• Homework programming assignments must be done individually.
• Must be submitted at the beginning of the class on the due date.
• Late submission penalty is 30%.
• No submission will be accepted beyond a week after the due date.

Students are supposed to select one project from the following table:

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.