CSci 5511, Spring 2008: Syllabus

Class Information

Room:: MechE 212
Time:: Tuesday and Thursday 4:00pm-5:15pm
Instructor:: Dr. Maria Gini (gini at cs.umn.edu)
office hours: Monday 2:00-3:00 and Wednesday 10:00-11:00 or by appointment in EE/CSci 5-213, (612) 625-5582.
Address: 4-192 EE/CSci Building, 200 Union St. SE, Mpls, MN 55455
Teaching Assistant:: Baylor Wetzel (wetz0025 at umn.edu)
office hours: Tuesday and Thursday 2:30-3:30.
Address: 2-209 EE/CSci Building, 200 Union St. SE, Mpls, MN 55455

Textbook

Stuart Russell and Peter Norvig "Artificial Intelligence. A modern approach. 2nd Edition", Prentice-Hall, 2003. (Chapters 1-11).

You'll need reference material on Lisp. Here are some choices:

Lamkins, Successful Lisp: How to Understand and Use Common Lisp, bookfix.com, 2004.
Graham, ANSI Common Lisp, Prentice Hall, 1996
The Lisp Code, the text of the first two chapters, and many articles on Lisp are available from http://www.paulgraham.com/

All class material will be posted at http://www.itlabs.umn.edu/classes/Spring-2008/csci5511/.

Prerequisites

Students are expected to have the following background:

Knowledge of basic computer science principles.
Knowledge of data structures (graphs and trees).
Knowledge of propositional and predicate logic.

Course Description

This course provides a technical introduction of fundamental concepts of artificial intelligence (AI). Topics include: history of AI, agents, search (search space, uninformed and informed search, constraint satisfaction, game playing), knowledge representation (logical encodings of domain knowledge, logical reasoning systems), planning, and the language Lisp. The course is suitable for students who want to gain a solid technical background and as a preparation for more advanced work in AI.

Course Requirements

four written homeworks (40% of the final grade). Homeworks will include problem solving and short programming questions;
one project (10% of the final grade). The project is on a topic of your own choice and can be done in groups of two.
participation to class discussion and class activities (10% of the final grade).
two in class midterm exams (each 10% of the final grade);
one final exam (20% of the final grade).

Academic Integrity

All work submitted for this class must represent your own individual effort (unless group work is explicitly allowed). You are free to discuss course material and approaches to problems with classmates, the TA, and the professor (and you are encouraged to do so), but you should never misrepresent someone else's work as your own. It is also your responsibility to protect your work from unauthorized access. Collaboration on homework or exams is cheating and grounds for failing the course. Any student caught cheating will receive an F as a class grade and the University policies for cheating will be followed. In addition, any graduate student caught cheating will be subject to the Department policy on cheating.

Policy on Exams and Grading

Grades will be assigned on the following scale: 93% and up will earn you an A 90% to 93% an A-, 87% to 90% a B+, 83% to 87% a B, 80% to 83% a B-, 75% to 80% a C+, 65% to 75% a C, 60% to 65% a C-, 55% to 60% a D+, 50% to 55% a D, below 50% an F.

Exams are open books and notes.
Homeworks are due in class. Late Homeworks will lose 10% of the maximum total points for every weekday late. Late homeworks must be submitted no later than one week after they are due (unless otherwise specified). Keys will be distributed in class a week after the homework is due.

Tentative Class Schedule

	Chapters	Topics	Assignments due	Slides from AIMA
Week 1 - Jan 22-24	1, 2	Introduction. Intelligent Agents.		Chapter 2
Week 2 - Jan 29-31	2, 3	Intelligent Agents. Problem Spaces.		Chapter 3
Week 3 - Feb 5-7	3	Search.	Homework 1 Tuesday Feb 5
Week 4 - Feb 12-14	4	Heuristic Search.		Chapter 4 (4.1, 4.2) the rest of Chapter 4
Week 5 - Feb 19-21	5	Constraint Satisfaction.	First Midterm Exam Tuesday Feb 19	Chapter 5
Week 6 - Feb 26-28	5, 6	Constraint Satisfaction. Game Playing.		Chapter 6
Week 7 - Mar 4-6	6	Game Playing.	Homework 2 Tuesday Mar 4
Week 8 - Mar 11-13	7	Propositional Logic.		Chapter 7
Week 9 - Mar 25-27	8	First-Order Logic.	Homework 3 Tuesday Mar 25 Project Proposal Tuesday Mar 25	Chapter 8
Week 10 - Apr 1-3	9	Inference in First-Order Logic.		Chapter 9
Week 11 - Apr 8-10	10	Knowledge Representation.	Second Midterm Exam Tuesday April 8
Week 12 - Apr 15-17	10	Knowledge Representation.
Week 13 - Apr 22-24	11	Planning.		Chapter 11
Week 14 - Apr 29-May 1	11, 12.3-12.7	Planning.	Homework 4 Tuesday Apr 29	Chapter 12
Week 15 - May 6-8	12.3-12.7	Planning.	Project Thursday May 8
Tuesday May 13		Final Exam, 4:00-6:00pm