CSci 4511w, Spring 2010: Syllabus

Class Information

Time/Room:	Tuesday and Thursday 2:30-3:45pm in ME 212
Instructor:	Dr. Maria Gini (gini at cs.umn.edu) office hours: Th 11:00-12:00 and Friday 2:00-3:00 or by appointment in EE/CS 5-213, (612) 625-5582. Address: 4-192 EE/CSci Building, 200 Union St. SE, Mpls, MN 55455
TA:	Will Groves (groves at cs.umn.edu) office hours: M 1:30 to 2:30 and W 4:45 to 5:45 in 2-209 EE/CS Baylor Wetzel (wetzel at cs.umn.edu) office hours: M 12:00-1:00 and T 1:00-2:00. in 2-209 EE/CS

Textbook

Stuart Russell and Peter Norvig "Artificial Intelligence. A modern approach. 3rd Edition", Prentice-Hall, 2010. (Chapters 1-12).
The third edition of the textbook has just been published. The second edition is out of print and hard to find. I will provide mappings between the two editions, so either one will work.

You should go to http://aima.cs.berkeley.edu/lisp/doc/install.html to download the Lisp software from the texbook. We will use it for some homeworks.

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

David B. Lamkins, Successful Lisp: How to Understand and Use Common Lisp, bookfix.com, 2004.
The author is asking people not to purchase a printed copy of his book, since the publisher is not paying royalties.
Peter Seibel Practical Common Lisp
another full book with on the web with multiple applications
Paul 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-2010/csci4511/.

Prerequisites

You are expected to have the following background:

Knowledge of basic computer science principles and programming.
Knowledge of data structures (graphs and trees).
Knowledge of formal logic (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, game playing, constraint satisfaction), planning, knowledge representation (logical encodings of domain knowledge, ontologies), and the programming language Lisp. The course is suitable if you want to gain a solid technical background and as a preparation for more advanced work in AI.

Writing Intensive Course

This course is writing intensive. In the course you will learn about writing in computer science and you will be asked to do different types of writing. You will be given the opportunity to get feedback and to resubmit some of the writings to improve your score.

Work Load and Grading Policy

Readings: Approximatively 30 pages of reading/week from the texbook and occasionally other papers.
Writings: there will be 5 pieces of writing you'll have to do plus a project report. Some of the writings will be related to the project, some will be short writings on other topics. Collectively, 20% of the grade will come from writing. To receive a passing grade in the course you need to get at least 60% of the score in the writings.
Participation: There will be an in-class exercise every week. Participation to the class activities will count for 13% of the grade.
Assignments:
- four written homeworks (each 6% of the grade). Homeworks will include problem solving and Lisp programming problems. We will assign five homeworks, so you can drop one of the scores or skip one.
  Late Homeworks will lose 10% of the maximum total points for every weekday late.
  Late homeworks will be accepted up to a week after they are due.
  Keys will be distributed in class a week after the homework is due.
- one project (15% of the grade: 7% for the writing part and 8% for the project content). The project is on a topic of your own choice and can be done in groups of two.
- five additional pieces of writing, 3 short writings, a literature review, and a draft of your project report (each 2%-3% of the grade).
Exams:
- two in class midterm exams (each 10% of the grade)
- one in class final exam (15% of the grade)
Exams are open books and notes.

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.

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 TAs, and the professor (and you are encouraged to do so), but you should never misrepresent someone else's work as your own. Discussing answers to problems and copying solutions from others It is also your responsibility to protect your work from unauthorized access. Discussing answers to problems and copying solutions from others in homeworks or exams is considered 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. The Regents Policy on Student Conduct, specially Section V, Subd. 1. Scholastic Dishonesty addresses these issues. You can find it at http://www1.umn.edu/regents/policies/academic/Student_Conduct_Code.html.

Tentative Class Schedule (subject to changes)

	Ch	Topics	Assignments due	AIMA Slides
Week 1 - Jan 19-21	1, 2	Intro. Intelligent Agents		Chapter 2
Week 2 - Jan 26-28	3	Problem Solving and Search	Writing 1, Tue Jan 26	Chapter 3
Week 3 - Feb 2-4	3,4	Search and Heuristic search	Homework 1, Tue Feb 2	Chapter 4.1-2
Week 4 - Feb 9-11	4	Other search algorithms (only 4.1 and 4.5)	Writing 2, Tue Feb 9	Chapter 4
Week 5 - Feb 16-18	5	Game Playing	Homework 2, Tue Feb 16	Chapter 5
Week 6 - Feb 23-25	17.5	Game Playing.	Writing 3, Tue Feb 23
Week 7 - Mar 2-4	6.1,6.3,6.4	Constraint Satisfaction.	First Midterm Exam Tue Mar 2	Chapter 6
Week 8 - Mar 9-11	7	Propositional Logic	Homework 3, Tue March 9	Chapter 7
Week 9 - Mar 23-25	8	First-Order Logic	Writing 4, Tue Mar 23	Chapter 8
Week 10 - Mar 30 - Apr 1	9	Inference in Logic	Homework 4, Tue Mar 30	Chapter 9
Week 11 - Apr 6-8	10	Planning	No assignment for Tue Apr 6	Chapter 10
Week 12 - Apr 13-15	10	Planning	Second Midterm Exam Tue Apr 13
Week 13 - Apr 20-22	12	Knowledge Representation	Writing 5, Tue Apr 20
Week 14 - Apr 27-29	18.3	Decision Trees	Homework 5, Tue Apr 27	Mitchell's slides
Week 15 - May 4-6		Q-learning	Project, Tue May 4	Mitchell's slides
Monday May 10		Makeup Final Exam	1:00-3:00, in ME 212
Saturday May 15		Final Exam	1:30-3:30, in ME 212