CSI 4106
Introduction to Artificial Intelligence


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, professorís 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 teamís 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 is10% 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.


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