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.
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.
Professor Nathalie Japkowicz, Office:
Office hours:
TBA
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.
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:
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:
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
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.
Topics: |
||
Week |
Topic |
|
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 |
|