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
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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).
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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.
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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.
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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
- Mechanism Design [Chap 17.7]
Machine Learning (Week 8-9)
- Introduction to ML [Chap 18.1, 18.2]
- Decision trees [Chap 18.3]
- Reinforcement Learning [Chap 21] not covered in detail.
Wrap-up (Week 10-11)
- Week 10 (12/06/06) Project Presentations
- Week 11 (12/13/06) Final Exam
- Some suggested practice exercises from Russel and Norvig [17.10,
17.12, 18.3, 18.4, 18.10]. Disclaimer: These are selected exercises. They do
not represent the entirety of the material that has been covered in lectures and that you are responsible for.
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Lecture Notes
- Lecture 1 (9/27/06) [PDF]
- To do: Begin thinking about your course project. See here for some project suggestions.
- Lecture 2 (10/04/06) [PDF]
- Lecture 3 (10/11/06) [PDF]
- Lecture 4 (10/18/06) No Class.
- Lecture 5 (10/25/06) [PDF]
- Lecture 8 (11/15/06) [PDF]
- Lecture 9 (11/29/06) [PDF]
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