Introduction to Artificial Intelligence, Spring 2003

CS 416

Time: Tuesday/Thursday 5:00 - 6:15
Place: THN E316
Instructor:
David Brogan (Olsson Room 217), dbrogan@cs.virginia.edu
Office Hours: Wednesday 1:30 - 3:00
Office Phone: 982-2211
Assistant:
Ben Hocking (Olsson Room 228 or Cobb Room 2028), abh2n@cs.virginia.edu
Office Hours: On Demand (via e-mail)
Web Page http://www.cs.virginia.edu/~cs416/
Prerequisites: CS 201, CS 202, Basic linear algebra, geometry, and calculus - CS 216 suggested
In previous years, the only prereq was CS 201 and CS 202. I'm strongly suggesting that students have completed CS 216 as well because programming is required for this course. Data structures (pointers, lists, and memory allocation) will be used extensively.
Textbooks: Artificial Intelligence, A Modern Approach, Russell and Norvig (2nd Edition)
Assignments: There will be three or four 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.
Homeworks: Three (perhaps four) programming assignments and a couple written assignments
Tests: One midterm and one final
Grading: Programs (40%) + Tests (25% and 25%) + Homework (10%)
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 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 students 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 semesters 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.


Date Topic Reading Slides
Week 1 Jan 16 Introduction Chess
Chapter 1
PowerPoint
PDF(6 slides/pg)
Week 2 Jan 21 Agents Chapter 2 PowerPoint
PDF (6 slides/pg)
Jan 23 Uninformed Searches Chapter 3 PowerPoint
PDF (6 slides/pg)
Week 3 Jan 28 Informed Searches Chapter 4 PowerPoint
PDF (6 slides/pg)
Jan 30 Informed Searches Chapter 4 PowerPoint
PDF (6 slides/pg)
Week 4 Feb 4 Informed Searches Chapter 4 PowerPoint
PDF (6 slides/pg)
Feb 6 Informed Searches Chapter 4
Assignment
PowerPoint
PDF (6 slides/pg)
Week 5 Feb 11 Advesarial Search Chapter 4 PowerPoint
PDF (6 slides/pg)
Feb 13 Adversarial Search Chapter 6 PowerPoint
PDF (6 slides/pg)
Week 6 Feb 18 Coding Review
Feb 20 Logical Agents Chapter 7
BugBrain
PowerPoint
PDF (6 slides/pg)
Week 7 Feb 25 Propositional Logic Chapter 7 PowerPoint
PDF (6 slides/pg)
Feb 27 Propositional Logic Chapter 7 PowerPoint
PDF (6 slides/pg)
Week 8 Mar 4 Spring Break
Mar 6 Spring Break
Week 9 Mar 11 First-Order Logic Chapter 8 PowerPoint
PDF (6 slides/pg)
Mar 13 Answers Midterm Exam
Week 10 Mar 18 First-Order Logic Chapters 8 & 9
Colloquium - Mar 28
PowerPoint
PDF (6 slides/pg)
Mar 20 First-Order Logic Chapter 9
Assignment
Answers
PowerPoint
PDF (6 slides/pg)
Week 11 Mar 25 First-Order Logic Chapter 9
PowerPoint
PDF (6 slides/pg)
Mar 27 First-Order Logic Chapter 9 PowerPoint
PDF (6 slides/pg)
Week 12 Apr 1 Neural Networks Chapter 20 PowerPoint
PDF (6 slides/pg)
Apr 3 Making Complex Decisions Chapter 17
Assignment
Answers
PowerPoint
PDF (6 slides/pg)
Week 13 Apr 8 Biologically Inspired Neural Networks Chapter 17
Levy Lab
PowerPoint
PDF (6 slides/pg)
Apr 10 Making Complex Decisions Chapter 17 PowerPoint
PDF (6 slides/pg)
Week 14 Apr 15 Making Complex Decisions Chapter 17 PowerPoint
PDF (6 slides/pg)
Apr 17 Making Complex Decisions Chapter 17
Assignment
PowerPoint
PDF (6 slides/pg)
Week 15 Apr 22 Statistical Learning Chapter 20 PowerPoint
PDF (6 slides/pg)
Apr 24 Hidden Markov Models Chapter 20 PowerPoint
PDF (6 slides/pg)
Week 16 Apr 29 Hidden Markov Models Chapter 20 PowerPoint
PDF (6 slides/pg)
Final May 6 7 pm Final Exam