SUBJECT DESCRIPTION FORM - Subject Code: COMP5511 - Artificial Intelligence Concepts

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Jul 17, 2012 (5 years and 2 months ago)

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Definitive

Programme Document

for

PG Scheme in Computing (2009/10)

Page
108


SUBJECT DESCRIPTION FORM



Subject
T
itle
:

Artificial Intelligence Concepts



Subject
C
ode
:

COMP5511



Credit
V
alue
:

3



Pre
-
requisite
: (Subject title and code no, if any)


Nil



Recommended background knowledge
:


Preferably has some experience with logic, computers and programming (familiar with data
structures and use of recursion as a program control structure).



Mutual Exc
lusions
:


This subject is not available to a holder of a degree (e.g. computing) which provided specific
knowledge overlapping the contents of this subject significantly or

Artificial Intelligence
Concepts (COMP501)



Learning
A
pproach
:


This course explor
es the core AI concepts. It provides a comprehensive introduction to the
problems and techniques of artificial intelligence. Theory and practice are both emphasized. To
enhance the understanding of how conceptions and ideas in AI are actually implemented,
prolog
and expert system shells will be used for programming exercises and projects. Lectures will be
supplemented with video sessions to enhance student's learning. A fair portion of guided reading
will also be provided.


42 hours of c
lass activities

incl
uding
-

lecture, tutorial, lab, workshop seminar

where applicable
.



Assessment
:

Continuous Assessment

45%

Test, and
Examination

55%



Objectives
:


Th
is subject aims to introduce the
ma
in concepts,
ideas
and techniques
of artificial intelligence

(AI)

to
t
he students
so that they could know the various aspects of AI, u
nderstand

some essential principles
and are

able to implement
some basic
AI
techniques
in their projects or other related work.




The Department reserves the right to update the syllabus cont
ents. Please note that the learning approach
for the same subject could vary slightly
due

to different delivery
modes
.

Definitive

Programme Document

for

PG Scheme in Computing (2009/10)

Page
109


Learning Outcomes
:


After completing the subject, students should be able to:


1.

u
se
logic programming

(e.g. Prolog) to write progra
ms to solve simple AI problems
;

2.

m
aster the basic searching techniques (e.g. breadth first search, depth first search, A search, etc)
for problem solving
;

3.

t
o know how to represent the knowledge and do reasoning
;


4.

t
o do reasoning in uncertainty
situations
;

5.

k
now how to use the basic machine learning technique
;


6.

t
o use artificial neural networks for data classification
; and

7.

k
now the basic techniques in computer vision and image understanding
.



Keyword syllabus
:


Logic Programming

Found
ations of
logic p
rogramming

and t
he PROLOG
l
anguage.


Problem Solving and Search Strategies

Uninformed search and b
asic
h
euristic
s
earch
strategies.


Knowledge Representation

Logic Representations,
Propositional logic, First order logic,
Automated
r
easonin
g


Reasoning in Uncertainty Situations

Non
-
monotonicity
,
Truth maintenance systems
,
Fuzzy logic
,
Bayesian reasoning


Artificial Neural Networks

What is ANN?

The architectures of ANNs.

What can ANN do?

How do ANNs learn?


Symbol based machine Learning

Vers
ion
s
pace
s
earch
,
Decision
t
ree
,
Explanation
-
based
l
earning
,
Unsupervised
l
earning


Selected
Advanced Topics

Natural Languages Processing,
Visual Image Understanding, Pattern Recognition, etc.



Indicative reading list and references
:


Bratko, I., 200
1
,
PR
OLOG, Programming for Artificial Intelligence
,
3
rd

ed., Addison
-
Wesley.

Luger, G.F., 200
9
,
Artificial Intelligence: Structures and Strategies for Complex Problem Solving
,
6
th

edition,
Addison
-
Wesley.

Russell, S. and Norvig, P., 2003,
Artificial Intelligenc
e
-

A Modern Approach
,
2
nd

edition,
Prentice
Hall.


Papers and articles selected from

Artificial Intelligence

AI Expert

AI Magazine

Applied Intelligence

IEEE Computer

IEEE Intelligent Systems and their Applications

IEEE Trans. Neural Networks