Instructor:
Dr. John R. Sullins
Office: 314 Meshel Hall
Phone: 742-1806
Email: john@cis.ysu.edu
Web site: http://cis.ysu.edu/~john/
Check the web site regularly, as assignments and announcements will be posted here.
Office hours:
Monday
Wednesday
Friday
Or by appointment
Objectives:
Understanding the foundations of Artificial Intelligence, including:
Prerequisites:
The official prerequisites are CSIS 3700 and CSCI 3710, or CSCI 6901. As far as content, the knowledge that you will need coming into this course is (1) the ability to write data-structure level programs in C/C++/Java, and (2) a good understanding of propositional logic and graphs/trees.
Textbook:
Artificial Intelligence: A
Modern Approach (third edition), Stuart Russell and Peter Norvig, Pearson. ISBN-13: 978-0-13-604259-4.
Given the dynamic nature of this field, not all material introduced by this
course may be covered in the textbook. Where appropriate, I may provide my own
notes for some topics.
Grading:
|
Homework assignments |
30% |
|
|
Exam 1 |
15% |
Date TBA |
|
Exam 2 |
15% |
Date TBA |
|
Research project/paper |
20% |
Due final week of class |
|
Final Exam |
20% |
Date TBA, Finals week |
Last day to withdraw with a "W": Thursday, October 28
Homework Assignments:
The homework assignments may involve a combination of written problems and some simple programming (probably in C++/Java) related to the application of core AI concepts.
Research Project/Paper:
You will be required to do either a research paper or a programming project (your choice) for this course:
· The research paper should be 15-20 pages long, and should survey the current state of some important or interesting area of Artificial Intelligence.
· The programming project is to be an implementation (in the language of your choice) of a program (such as a game) based on some AI-related algorithm.
In either case, you will be asked to give a short presentation on your paper or project during the last week of class. More details will be available as the semester progresses.
Graduate Students:
Graduate students will be required to do both a research paper and a programming project on some AI-related topic. Specifically, you will implement one of the AI algorithms and report on its current use and open issues.
Tentative Course Outline:
|
WEEK |
TOPICS |
TEXTBOOK |
|
8/23 |
Introduction to AI and intelligent agents |
1, 2 |
|
8/30 |
State space representation of problems, Heuristic search |
3 |
|
9/6 |
Search in complex and unknown environments (no class Monday) |
4 |
|
9/13 |
Problem solving as constraint satisfaction |
6 |
|
9/20 |
Logical agents and automated inference |
7, 9 |
|
9/27 |
Problem solving as planning |
10 |
|
10/4 |
Creating plans in complex and unknown environments |
11 |
|
10/11 |
Representing uncertain knowledge; Probabilistic reasoning |
13, 14 |
|
10/18 |
Bayesian networks; Temporal reasoning |
14, 15 |
|
10/25 |
Utility theory for decision making with uncertain knowledge |
16, 17 |
|
11/1 |
Automated learning from examples |
18 |
|
11/8 |
Knowledge-based learning |
19 |
|
11/15 |
Probabilistic and reinforcement learning |
20, 21 |
|
11/22 |
Advanced topics (time permitting) |
|
|
11/29 |
Project/Research paper presentations |
|
|
12/6 |
Final Exam |
|