UNIVERSITY OF MONTREAL
Artificial Intelligence
Optional 4 credit course
Professor: Esma Aimeur
Office 2361
SCHEDULE
Monday (3:30PM to 5:30PM) room Z207, Pavillon Claire McNicoll
Wednesday (4:30AM to 6:30PM) room 1207, Pavillon André Aisenstadt
INTRODUCTION
Thinking computers are not a new concept and we are already
used to the idea! But how exactly do they think? How
can they memorize different situations, classify plants,
diagnose diseases, or even morph into teachers to provide adapted teaching
to the student? More importantly, how can they excerpt and organize this
large amount of information (often wrong or contradictory) provided
by their human operators?
Artificial intelligence.... yes, but beyond powerful microprocessors and lines of code and
beyond the computer itself, isn't the most important part of computer science
the end-user himself? So, how does it think? What are it's goals and needs?
The way to excerpt and organize large amount of information that the user
has (whether he is a cardiologist or a child), is certainly
one of the greatest challenges in the domain of modern computer science.
If this is the type of challenge you are looking for, then this course is for you.
COURSE DESCRIPTION
The course will begin with a general introduction to Artificial Intelligence and then focuses on
Knowledge Based systems.
I will then go on to Intelligent Agents which are software entities that operate on their
own and have the following characteristics: reactivity, autonomy, adaptability, cooperation.
I will introduce some studies on Human Learning (Intelligent Tutorial Systems)
the emphasis being on human-machine interaction. This will lead us onto cooperation,
collaboration and conflicts between agents.
We will also look at the following topics in Machine Learning:
Inductive Learning (from examples or observations), Deductive Learning,
and Learning by Analogy. Learning by analogy is a type of learning whereby a system must remember
previous cases stored in it's database to solve a new case. This is referred to as case-based-reasoning.
To acquire knowledge we must extract knowledge and organize it with
manual, semi-automatic (interactive), or automatic techniques.
Students will also learn about the different interactive acquisition techniques
(knowledge elicitation), as well as direct techniques (interview
techniques, direct observation, protocols analysis technique) and indirect
techniques ( repertory grid, sorting cart, the laddering technique ),
showing their basis in cognitive psychology studies.
If you are interested, please fill in this form
PREREQUISITE
It is preferable, but not mandatory, that you have completed
the artificial intelligence course.
EVALUATION
All students will have to submit a summary of articles accompanied by an oral
presentation, a team programming project, and a written exam on a specific part
of the course.
The allowed programming languages are:
C,
C++,
or JAVA.
Project : 40
%
Article summary and oral presentation : 40 % (25 % for the summary and 15 % for the the oral presentation)
Exam (oral or written) : 20 %
REFERENCES
There is no mandatory book for this course, but I recommend these books:

Russell, S., Norvig P. (2003). Artificial Intelligence : a Modern Approach. Prentice-Hall

Turban, E., King, D. (2012) Electronic Commerce, A Managerial Perspective. 7th edition, Prentice-Hall.

Tipton, H. F., Krause, M. (2004). Information Security Management Handbook. 5th edition, Auerbach.

Bryant, A. (2006). Thinking Informatically: A New Understanding of Information Communication, and Technology. Edwin Mellen Press.
PRESENTATIONS
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