CSI 4106
Introduction to Artificial Intelligence

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Description

Basic concepts and methods of Artificial Intelligence. Representation of knowledge. Natural language processing. Games and search strategies. Planning. Deduction and reasoning. Machine learning. Basic notions of expert systems.

Objectives

When you will have completed this course, you will be able to:

· Consider a complex problem and find a way to represent it in a form compatible for computer processing.

· Apply or design an appropriate technique for solving this problem automatically or semi-automatically.

· Understand the terminology and the techniques currently in use in the field of Artificial Intelligence.

Instructor

Professor Nathalie Japkowicz, Office: STE 5-029,

Office hours:

• Mondays, Wednesdays 1-2pm
• By arrangement

TA

TBA

Course Times

· Wednesdays 16h00-19h00, Location: VNR 283

Pre-requisites

• (CSI3120 or SEG2106) and MAT1348CSI3525
• or, professor’s permission.

TextBook:

• Luger, George, F.: Artificial Intelligence, Structures and Strategies for Complex Problem Solving , Addison Wesley, Fifth Edition, 2002.

Student Evaluation:

• Assignments/Labs---------------------------30 %
• Project Report/Presentation-----------------15 %
• Mid-Term Exam----------------------------20 %
• Final Exam----------------------------------35 %
• Total:--------------------------------------100 %

Assignment, Presentations, Project Reports and Exams:

Assignments
Assignments must be handed in at the beginning of classes, the day they are due. There are no make-up assignments. The three assignments will have to be handed in on the following days. They will be posted two weeks before their due-date.

• Assignment #1 (LISP/Search) ----- Due Date: Wednesday, February 6, 2008
• Assignment #2 (PROLOG/Logic) -------Due Date: Wednesday, March 5, 2008
• Assignment #3 (WEKA/Learning) ------Due Date: Wednesday, April 2, 2008
• Project Report (On a topic of your choice) ------Due Date: On Presentation Day

Presentation and Report
Students, in teams of two, will do a project on the practical applications of Artificial Intelligence. This will involve carrying out research on the topic of the team’s choice, submitting a report on this research, and giving an in-class presentation of 15 or so minutes, during which both team members will have to speak. You can choose a topic from one of the following areas of application:

• Computer Games (A very popular topic, in general !)
• Expert Systems
• Robotics
• Planning
• Natural Language Processing
• Machine Learning/Data Mining
• Neural Networks
• Genetic Algorithms
• AI and Psychology

Click HERE for a schedule of presentations.

LATE HAND-IN POLICY: Late hand-in will be accepted for several days after the due date. The late penalty is 10% a day for weekdays and 5% a day for weekends and holidays.

Exams
There will be two exams: a mid-term and a final. Here are their dates:

• Mid-term---------- Wednesday, February 13, 2008 (in class)
• Final--------------- TBA

It is compulsory to write the mid-term exam. There will be no make-up exam. If you have a valid medical reason to explain your absence from the mid-term (this reason must be confirmed by the University Health Services), I will add the percentage representing the value of the mid-term to that of the final exam. Otherwise, if you do not write the mid-term, you will receive a 0 on it.

A policy of the School of Information Technology and Engineering requires you to pass the controlled evaluation component, that is to say, to achieve at least 50% of the available test and exam marks. The grade will be calculated according to the usual University of Ottawa and Faculty of Engineering scale (A+ for 90% and above, failure with a D or lower for less than 55%). The basis for the grade will be the adjusted total AT, computed from MD (midterm), FN (final) and AS (assignments), as follows:

if MD + FN < 27.5
then AT = (MD + FN ) * 1.5
else AT = MD + FN + AS;

Course Material
All the course slides, a description of plagiarism and its consequences, as well as your assignments and other material are available HERE.

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Lecture Plans:

Topics:
Week	Topic	Readings
January 7	What is Artificial Intelligence?	Chapter 1
	Knowledge Representation and Search	Section 3.0-1
January 14	Basic Search Techniques	Section 3.2
	Heuristic Search	Section 4.0-2
January 21	Games	Section 4.4
	Games and Complexity Issues	Section 4.4-5
	Assignment 1 handed in: LISP/Search	Due date: February 6
January 28	Propositional Logic	Section 2.1
	Predicate Logic	Section 2.2
February 4	Predicate Logic	Section 2.2
	Mid-term review
February 11	Mid-Term Exam
	Proofs by Resolution	Sections 2.3 and 2.4
	Assignment 2 handed in: PROLOG/Logic	Due Date: March 5
February 18	Study Break
	Study Break
February 25	Frames and Semantic Networks	Excerpts from Sections 7.0-2
	Expert Systems	Excerpts from Sections 8.0-2
March 3	Representing uncertain knowledge	Sections 9.0-2
	Machine Learning	Sections 10.0-2
March 10	Machine Learning	Section 10.3
	Presentation Session	(4 Presentations)
	Assignment 3: C4.5/Machine Learning	Due Date: April 2
March 17	English and Natural Language Processing	Chapter 14
	Presentation Session	(4 Presentations)
March 24	Natural Language Processing: Semantics	Chapter 14
	Presentation Session	(4 Presentations)
March 31	Planning	Section 8.4
	Presentation Session	(4 Presentations)
	Presentation Session	(4 Presentations)
	Final Exam Review