Course Description: This course provides a generic
introduction and explanation of the most prominent branches of the science of
Artificial Intelligence (AI). One of the aims of this course is to introduce
the undergrad students to the concept of devising and implementing
research-based projects, i.e., those projects which have the potential to be
presented as research work. Topics covered will include the operation of
intelligent agents, intelligent search (solution-discovering) algorithms and
constraint satisfaction problems, First-Order Logic and its inference,
Knowledge Representation, Planning, Uncertainty and Bayesian Networks, and
Machine Learning. For all these topics, students will be introduced to the current
implementation techniques and research trends, in order to motivate them to
develop these trends further.
Course Goals: This course aims to provide the students with a concrete grasp of the fundamentals of various branches that currently
constitute the field of Artificial Intelligence, e.g., autonomous planning, autonomous control, data mining,
natural language processing etc. It also aims to train
you into devising and implementing a research-based AI Project. You will learn how to: 1) Capture the state-of-the-art of some AI branch, 2) Detect its limitations, and 3) Propose new solutions (and implement them). Important: The students should be able to generate enough novel and concrete solutions that would allow their work to be published in some conference/workshop.
Sections
and Lectures: 2 Sections (A and B) with 6 lectures/week
Primary
Textbook: Artificial
Intelligence: A Modern Approach (2nd Edition), by Stuart
Russell and Peter Norvig
Reference Textbooks: Artificial
Intelligence: A Guide To Intelligent Systems (2nd Edition),
by Michael Negnevitsky, and Agent Technology For
Communication Infrastructures, by Alex L. G. Hayzelden and Rachel A.
Bourne.
Assignments and Projects: The students would be required
to divide themselves into groups. Both the assignments and the project are to
be submitted collectively by the whole group. There would be three assignments,
pertaining to the current contents that being taught in the course, with weightages
of 3%, 3% and 4% respectively.
Cheating Policy: Simply put, any two or
more matching assignments will be marked directly with a 0. No compromise or
consultation would be permitted in this regard.
Attendance Policy: This is very strict.
Absolutely no compromise would be made for those students who are not motivated
or serious enough to attend classes. My own policy is that if you find the
course uninteresting despite the efforts of the teachers, then you should drop
the course, rather than hanging on and bunking classes.
Class Discipline Policy: Any student who is
disrupting the environment of the class will simply be asked to leave. If this
student persists with disruptive demeanor, then he/she will be permanently
disallowed from attending any further classes. No compromise or complaints
would be entertained in this regard.
Lectures