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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