Day / Time: Thusday 17:45-21:35pm
Credit Hours: 3
Prerequisite: MCS 2534 (Data Structures) and (C++ or Java)
Lecture Room: S207
Lab.: CW21 (for some lectures, and 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.)
• Black Board for the class
• Office Hours: check out http://www3.ltu.edu/~chung/se01sch.htm

• Title: Artificial Intelligence: A New Synthesis
• ISBN 1-55860-467-7, March 1998
• Author: Nils J. Nilsson

• Title: Artificial Intelligence, 3/e, 1992, 750 p., ISBN 0-201-53377-4
• Author: Patrick Henry Winston, Massachusetts Institute of Technology

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, methodologies, and algorithms (simulation of intelligence in computers) (53%)
• Applying theories and techniques to the development of practical Intelligent Systems as class projects (45%)
• To plan MSCS capstone project I and II (2%)

Introduction: Fundamental issues in Intelligent Systems (Chap 1) [0.5 week]

Search and Optimization methods (Chap 7, 8, 9, 11, 12) [3.5 weeks]

• Generate and Test and Problem Reduction

• Nets and Space-State Search

• Basic Search (Uninformed Search), Heuristic Search and Optimal Search

• Trees and Adversarial Search

• Constrained Search, Constraint satisfaction

• Nonlinear Function Optimization

• Planning

Representing knowledge and reasoning methods (Chap. 13, 14, 15, 16, 17, 18, 19) [1.5 weeks]

• Knowledge-based systems
• Representing common sense knowledge, space, time, events, and actions
• Reasoning: predicate calculus and resolution, rules and rule chaining, logic, probabilistic reasoning, Bayes' theorem, reasoning with uncertainty 

Learning, Adaptation, and Reactive Machines (Chap. 2, 3, 4) [3.5 weeks]

• Decision tree learning

• Introduction to Version Space

• Learning by Training Perceptrons

• Learning by Training Artificial Neural Nets

• Learning by Simulating Evolution

Autonomous Robotics and Robot Vision (chap. 6, +) [1 week]

Problems to solve as individual home works: The price is right game, non linear function optimization, boole problem, scheduling/time tabling 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

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 by default; 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.