Introduction to Artificial Intelligence

CS510

Fall 2005

Wed 6:00pm - 8:50pm


Instructor

Dr. Jay Modi
Department of Computer Science
Drexel University
Office: University Crossings 106
Tel: 1 215 895 1518
Email: pmodi AT cs drexel edu
Office Hours: Thurs 3:00-4:00 or by appt

Course Overview

This course is about the theory and practice of constructing systems (machines) that can be considered intelligent. The course will strive to cover both theoretical aspects of AI (readings) and practical aspects of AI (programming and implementation).

Prerequisites

  • A serious interest in AI.
  • Basic competency in computer science including data structures and programming, and basic competency in mathematics including proof techniques such as induction.

Textbook

Russell and Norvig, Artificial Intelligence: A Modern Approach , the Prentice Hall Series in Artificial Intelligence. ISBN 0-13-790395-2.
Second Edition (green color)

We will follow the book fairly closely but not cover all of it, and may cover some topics not in the textbook, in which case supplemental readings will be assigned.

Coursework and Grading

Grading will consist of a midterm exam, final exam and course project. The midterm and final will be written, in class and cover topics from the textbook and lectures. The course project will be a self-designed project involving implementation, experimentation and in-depth investigation of a key topic in AI. The topic of the project and its parameters are to be determined through agreement between instructor and student. A final written report and final presentation to the class will form the basis of the project grade. Projects may be done individually or in teams of up to 2 people. If project is done in a team, both members will get the same grade and so both must participate equally. Choice of programming language is up to you.

Below is the grading breakdown:

  • Midterm: 25%
  • Final: 25%
  • Project: 40%
  • Class participation: 10%

Schedule

Note: This schedule is tentative and can change.

Introduction To AI and Agents (Week 1)

  • Problem solving, rationality [Chap 1,2]
  • Types of Agents
  • PEAS

Search (Week 2-4)

  • Uninformed search (BFS, DFS, Bi-directional) [Chap 3]
  • Informed search (A*, IDA*) [Chap 4.1,4.2]
  • Local search (Hillclimbing, GA/GP) [Chap 4.3]
  • Constraint Satisfaction and Search [Chap 5]

AI and Games (Week 5-7)

  • Intro to game theory [Chap 6, Chap 17.6]
  • Minimax trees
  • Alpha-beta pruning

Machine Learning (Week 8-10)

  • Introduction to ML [Chap 18]
  • Decision trees
  • Reinforcement Learning [Chap 21]

Lecture Notes

  • Lecture 1 (9/28/05) [PDF]
    • Homework 1: Define your course project. Due 10/12/05. See here for project suggestions.
  • Lecture 2 (10/05/05) [PDF]
  • Lecture 3 (10/12/05) [PDF]
    • Homework 2: Practice exercises on search. Do not hand in. See [PDF].
  • Lecture 4 (10/19/05) [PDF]
  • Lecture 5 (10/26/05) [PDF]
  • Note: Midterm exam on 11/2/05.
    • Suggested practice exercises from Russel and Norvig [2.2, 2.5, 3.1, 3.8, 4.1, 4.2, 4.4, 5.3, 5.5b, 5.6]. Do not hand in. Solutions are available from instructor's office or via email request.
  • Lecture 7 (11/09/05) [PDF]
    • Homework 3: Prepare a project update presentation to give to class. (5 min or 2 slides). Due 11/16/05.
  • Lecture 8 (11/16/05) [PDF]
    • Suggested practice exercises from Russel and Norvig [6.2, 6.3abc, 17.9, 17.13, 18.3]. Do not hand in.
  • Lecture 9 (11/30/05) [PDF]
  • Lecture 10 (12/07/05) Project Presentations
  • Note: Final exam on 12/14/05.
    • Final project reports also due.
  • 12/16/05 Grades due. Last day of semester!! Have a great holiday.