Fall 2006   MCS 5503

Intelligent Systems (Introduction to Artificial Intelligence)

Lawrence Technological University, Department of Math and Computer Science

 

 

 

Day / Time: Wednesday, 17:45-7:00 and 7:10-8:25 pm
Credit Hours: 3
Prerequisite: MCS 2534 (Data Structures) and (C++ or Java)
Lecture Room: S326
Lab.: CW21 or later M219 (for some lectures, and if you choose robotics project)

Course Objective:

This course provides an introduction to artificial intelligence and computational intelligence.

 

Course Description:

Topics covered include problem solving by searching, Adversarial Search, optimization methods, knowledge representation and reasoning, machine learning, multi-agent systems, planning, image processing and pattern recognition, evolutionary computation, and artificial neural networks.

 

Instructor: CJ Chung, Ph.D.

  • Office Room: Science 112,   Office Hours: Wed. 4-5 pm.
  • 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, Personal webpage at http://qbx6.ltu.edu/chung (syllabus, etc.)
  • my.ltu.edu - Black Board (Discussion Board, Possibly Chat, and Online Quiz, etc.)

Textbook: Artificial Intelligence: A New Synthesis by Nils J Nilsson, Morgan Kaufmann Publishers, March 1998, 1-55860-467-7

Recommended Text

 

Internet Resources

Course Goals

  • To learn the foundations of Artificial Intelligence, Machine Learning, Computational Intelligence, SoftComputing (simulation of intelligence in computers) (60%)
  • To apply theories and techniques to the development of practical Intelligent Systems, mostly related to robotics, as class projects (40%)
  • To plan next classes such as MCS 6513 Advanced Topics in Intelligent Systems, MSCS capstone project I and II for MSCS students

 

Class Topics  [16 weeks + 1 final week]

·         Introduction: Introduction to Artificial Intelligence and Fundamental issues in Intelligent Systems (Chap 1) [1 week]

·         Search and Optimization methods (Chap 7, 8, 9, 10, 11, 12) [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 Numerical Function Optimization

Combinatorial Optimization

Planning

Introduction to multi-objective function optimization

·         Learning, Adaptation, and Reactive Machines (Chap. 2, 3, 4) [5 weeks]

Decision tree learning

Introduction to Version Space

Learning by Training Perceptrons

Learning by Training Artificial Neural Nets

Learning by Simulating Evolution

·         State Machines, Autonomous Robotics and Robot Vision (chap. 5, 6, +) [3 week]

·         Introduction to knowledge representation and reasoning methods (Chap. 13, 14, 15, 16, 17, 18, 19) [1 week]

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 

Introduction to Fuzzy Logic and Fuzzy Inference Systems

·         Multiple Agent (Chap. 23) [1 week]

 

Tentative Schedule 

 

Date

Topics

Note

8/30

Introduction to AI and Intelligent Systems

First day of Class

10/11

 

Possibly Online Class

10/18

Midterm

5:45-7:20 pm

11/15

Practice Thanksgiving Robot Parade

Project

11/17

Thanksgiving Robot Parade

Saturday morning

11/22

Last day to withdraw

 

12/6

Project Demonstrations begin in Class

 

12/20

Final Exam, Class Competition, and Project Demo

 

 

Grading: Total 200 points

  • Homework assignments and project(s) - 100 points
  • Online quizzes: 10 points
  • 1 midterm: 40 points 
  • 1 final (everything covered in class) - 50 points

This score will be translated into a letter grade based upon the percentages given below. (F will be given to Grad students, if under 69%)

 

A 

90-100%

C 

70-74%

A- 

89% 

C-

69%

B+   

85-88%

D+

65-68%

B 

80-84%

D 

60-64% 

B-  

79%

D-

59%

C+   

75-78%

F 

00-58%



Class Policies

  • Attendance is essential to doing well in the course. The exam will focus primarily (but not exclusively) on material presented in the lectures.
  • If you are unable to attend a meeting (on-line meeting), it is your responsibility to obtain the material from other students, instructor, or from the web.
  • Class events may be photographed and/or videotaped. Students are expected to give permission for this material to be printed, published, posted on the websites, and/or televised in the public forum.

Written Examination Policies

  • There will be no makeup exams.
  • Closed books, closed notes by default; and closed neighbors.
  • See Policy on Academic Misconduct section below.

Homework Policies

  • Homework programming assignments must be done individually, in general.
  • Source codes must follow good programming standards such as commenting, indentation, and meaningful names.
  • Must be submitted before or at the beginning of the class on the due date.
  • Read the submission instruction carefully for each homework
  • Some home works are for class competitions. Winners will be recognized in various ways.
  • See the “Policy on Late homework or project” and “Policy on Academic Misconduct” sections below.

 

Class Projects

Each student is expected to select a project from a list of suggested (group) projects that will be given by the instructor. A student can bring her/his own project, which must be approved by the instructor. Group projects may be possible, depending on the subject, size and scope. See the “Policy on late homework or project” and “Policy on Academic Misconduct” sections below.


Policy on Late homework or project

  • Full credit at the beginning of class on the due date
  • 10% deduction per day (24 hours)

Intellectual Property and Copyrights

All the deliverables may be reused/modified/upgraded by another students and/or instructor later on for educational purposes. The instructor will make sure to give appropriate credits and acknowledgements to the student in that case. The instructor believes that the student has the intellectual property rights of the software student wrote. However, since it is done in a class at LTU, it is also requested that the student should give appropriate credits and acknowledgements to the University as well as the instructor, if the software is used or commercialized after the class.

 

Policy on Academic Misconduct

Each student must comply with the University Academic Honor Code at http://www.ltu.edu/currentstudents/honor_code_offenses.asp

 

8/30/06