Introduction to Artificial Intelligence

Introduction to Artificial Intelligence, Fall 2004

CS 416

Time:

Monday/Wednesday 5:00 - 6:15

Place:

Olsson 009

Instructor:

David Brogan (Olsson Room 217), dbrogan@cs.virginia.edu
Office Hours:	TBD
Office Phone:	982-2211

Assistant:

Ben Hocking (Olsson Room 228 or Cobb Room 2028), hocking@virginia.edu
Office Hours:	Sign Up

Web Page

http://www.cs.virginia.edu/~cs416/

Discussion
Forum:

http://www.cs.virginia.edu/~humper/forums/

Prerequisites:

CS 201, CS 202, CS 216. Calculus required. Basic linear algebra and statistics recommended, but will be reviewed in class. As this course is intended for upper-class computer science majors, the CS 216 prerequisite represents a minimal amount of programming skills. The programming will be C/C++ and the class assignments will require significant programming efforts.

Textbooks:

Artificial Intelligence, A Modern Approach, Russell and Norvig (2nd Edition)

Programming
Assignments:

There will be three programming assignments in this course. All assignments must be written in C or C++. The program source code will be read. Source code documentation and organization should make your programs easy to read and convey your understanding of the implemented functions. Poor documentation and programming style will result in a lower score. More detailed instructions regarding required documentation will be provided with each assignment.

Programming
Project:

There will be one programming project that will be due at the end of the semester. This project will require the creation of a program that will participate in a gaming competition. Game TBD.

Homeworks:

Roughly four homework assignments will reinforce the classroom material.

Tests:

One midterm and one final

Grading:

Programs (3@10%) + Project (15%) + Tests (2@20%) + Homework (3@5%)
View Gradebook

Late Days:

Students have five late days that they can use in any way during the semester. Each late day extends the due date for a homework or programming assignment by 24 hours. Use your late days wisely; you will not be granted additional late days without a written note from the Dean's office.

Honor Code:

The honor code applies to all work turned in for this course. In particular, all code and documentation should be entirely your own work. You may consult with other students about high-level design strategies related to programming assignments, but you many not copy code or use the structure or organization of another student's program. Said another way, you may talk with one another about your programs, but you cannot ever look at another student's code nor let another student look at your own code. Each assignment will include a specific Honor Code Guideline referring to the use of online materials.

Lectures:

The following topics will be presented during the semester's lectures. This is only a rough outline of the schedule and entire topics may be added or removed. The class web page will document the lecture schedule and provide access to the slides used for each lecture. Consult it often.

Learning
Outcomes:

At the end of the semester, students should have accomplished the following learning outcomes:

Knowledge of fundamental and modern AI research accomplishments
Evaluate AI techniques and synthesize solutions to practical examples
Enhanced software design and implementation skills
Exposure, comprehension, and application of mathematical skills to computer science

	Date	Topic	Reading	Slides

Week 1	Sep 1	Introduction	Ch. 1	PowerPoint Movies
Week 2	Sep 6	Agents	Ch. 2	PowerPoint
	Sep 8	Uninformed Searches	Ch. 3	PowerPoint
Week 3	Sep 13	Informed Searches	Ch. 4	PowerPoint
	Sep 15	Informed Searches	Ch. 4 Tree Search Out	PowerPoint
Week 4	Sep 20	Informed Searches	Ch. 4	PowerPoint
	Sep 22	Informed Searches	Ch. 4 GA code (MATLAB) Office Hours Signup	PowerPoint
Week 5	Sep 27	Informed/Adversarial Search	Ch. 6	PowerPoint
	Sep 29	Logical Agents Propositional Logic	Ch. 7	PowerPoint
Week 6	Oct 4	Propositional Logic	Ch. 8 Tree Search Due PL Homework Out	PowerPoint
	Oct 6	First-order logic	Ch. 9	PowerPoint
Week 7	Oct 11	"Reading Holiday"
	Oct 13	First-order logic Last day to drop...	Ch. 9	PowerPoint
Week 8	Oct 18	Video: "The Machine That Changed the World" Parts 3 (Sketchpad) and 4 (AI).		PowerPoint
	Oct 20	Probability	Ch. 13 PL Homework Due	PowerPoint
Week 9	Oct 25	Midterm		Study Guide 2003 Midterm 2003 Answers MIDTERM 2004 ANSWERS
	Oct 27	Bayes Video: "The Machine That Changed the World" Part 4 (AI)	Ch. 13	PowerPoint
Week 10	Nov 1	Bayes Video: Sampling Plausuble Solutions to Multi-Body Constraint Problems. Chenney et al., SIGGRAPH 2000.	Ch. 14 Bayes' Assignment Out	PowerPoint
	Nov 3	Reasoning with Bayes'	Ch. 15	PowerPoint
Week 11	Nov 8	Clustering	p. 725-726	PDF
	Nov 10	Reasoning with Bayes'	Ch. 15	PowerPoint
Week 12	Nov 15	Review of Bayes' and programming assignment	Ch. 17 Bayes' Assignment Due
	Nov 17	Reinforcement Learning	Ch. 17 Gambler's Ruin Out	PowerPoint
Week 13	Nov 22	Reinforcement Learning and Game Theory	Ch. 17	PowerPoint
	Nov 24	Thanksgiving Recess
Week 14	Nov 29	Neural Networks	Ch. 21.5 Gambler's Ruin Due NN HMWK Solution NN Program Out	PowerPoint
	Dec 1	Neural Networks	Ch 21.5	PowerPoint
Week 15	Dec 6	Cool applications of AI	Lots of Videos	PowerPoint
	Dec 8	NO CLASS
Final	Dec17	FINAL EXAM -- 7:00 p.m. in OLS 009		Final Exam

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