Course Information
Text
AI: A Modern Approach, Stuart Russell and Peter Norvig, 2nd ed
(
See below for additional readings)
Prerequisites
ESO 211 Data Structures. Optional: Probability and Statistics, familiarity with logic.
Course Objective
Artificial Intelligence tries to have machines do
things that are normally associated with intelligence in humans. In the
process, this also sheds light on the processes of human cognition.
This course will introduce these topics through lectures, class presentations
and discussions, and most importantly, through projects. Each of you is
also expected to select a project in which you will investigate some
topic of current research interest, and you are expected to be able
to communicate the key ideas of your project to others in the course.
Grading Scheme
- Two Written Exams: 40%
- Course Discussions, Homework and Labs: 10-15%
-
Final Project: 45-50%
- Proposal: 5%
- Presentation: 10%
- Report: 15%
- Demo/Oral: 20%
(approximately)
Projects
-
Projects from past years, assignments, solutions, and other
details may be seen
here.
-
Some projects with a Machine Learning focus which can be
found here may also be relevant.
-
Owing to the high project weightage, project groups will be formed
by lottery within the first two weeks of class.
Course Topics
For some topic, there will be additional presentations by students.
| Topic |
Week |
References |
|
INTRO: AI as Complexity; Managing complexity; agents, symbolic systems
|
week 1 |
ch.1,2 |
|
SENSING: Vision - Image Formation, Gradient and Motion cues,
Learning Backgrounds, Tracking
|
week 2 |
ch.24,
marr ch.1 |
|
ACTION: Robotics - articulated and mobile robots;
motion planning, task planning.
|
week 3 |
ch.25 |
|
Problem Solving As SEARCH / Games
|
week 4 |
ch.3,5 |
|
Rational Agents: Logical reasoning and Knowledge Representation
|
week 5-6 |
ch.7,8,9,12 |
|
LEARNING. Learning logical rules
|
week 7 |
ch.18, 19 |
|
PROBABILISTIC LEARNING: Unsupervised (clustering) / Supervised (Bayesian and
SVMs); Learned knowledge representations (manifolds)
|
week 8-10 |
ch.20,
bishop ch.1; lee-verleysen p.1-15; |
|
NATURAL LANGUAGE PROCESSING: Language structure, parsing, probabilistic
grammars
|
week 11 |
ch.22 |
|
AI : Future, Philosophy
|
week 12 |
ch.26-27 |
We shall have a three hour workshop on project proposal presentations around week 6.
Additional Readings
-
Marr, David;
Vision: A Computational Investigation Into the Human Representation and
Processing of Visual Information,
W.H. Freeman, 1982, 397 pages, ISBN 0716715678
-
Bishop, Christopher M.;
Pattern Recognition and Machine Learning,
Springer, 2006, 738 pages, ISBN 0387310738
-
Richard O. Duda; Peter E. Hart; David G. Stork;
Pattern Classification (2/e),
Wiley-India, 2007, 676 pages, ISBN 8126511168
-
Lee, John A.; Michel Verleysen;
Nonlinear Dimensionality Reduction,
Springer 2007, 310 pages, ISBN 0387393501
-
Research Papers