IS5152 Decision Making Technologies
Semester
2, 2010/11.
Tuesdays
,
6.30
-
8.30 pm
,
COM1/204.
Instructor: Dr. Rudy Setiono
Contact:
rudys@comp.nus.edu.sg
,
disrudy@nus.edu.sg
Office: COM2 04
-
13
IS5152 Decision Making Technologies
Course objective:
to introduce students to decision making technologies
that can support decision making in the financial, operational, marketing and
other strategic areas.
Description:
Over the past two decades, increasing research efforts have
been directed at finding new machine learning (ML) techniques for decision
making and their possible application in solving practical problems. ML
techniques such as artificial neural network methods have been proven to be
powerful tools for business decision making. Among the application
problems where ML techniques outperform traditional decision making
methods such as statistical methods are credit rating, bankruptcy analysis,
foreign exchange rate predictions and many others.
IS5152 Decision Making Technologies
Topics covered:
The techniques covered in this course include neural networks for
classification/regression/clustering, genetic algorithm for optimization,
decision tree methods, support vector machine, data envelopment
analysis and data mining.
Journal articles that present new techniques for decision making and/or
describe successful application of the existing methods in solving
practical problems will be discussed in class.
IS5152 Decision Making Technologies
This course requires the students to have some background knowledge in:
Calculus
Simple linear algebra
Basic probability and statistics
No
computer programming skill is required.
IS5152 Decision Making Technologies
Tentative
schedule:
Week 1
January 11, 2011
Introduction and class administration
Week 2
January 18, 2011
Decision making under uncertainty
Week 3
January 25, 2011
Optimization
and decision making
Week
4
February
1, 2011
Support vector machines
Week 5
February
8, 2011
Decision making with multiple objectives
Week 6
February 15, 2011
Data envelopment analysis
February 22,
2011
No lecture. Mid
-
semester
break
Week 7
March 1, 2011
Mid
-
semester exam.
Week 8
March 8, 2011
Decision making with decision trees and rules
Week 9
March
15, 2011
Neural networks for decision making (Part 1)
Week 10
March
22, 2011
Neural networks for decision making (Part 2)
Week 11
March 29, 2011
Rule generation
from neural networks
Week 12
April 5, 2011
Genetic algorithms for decision
making
Week 13
April 12, 2011
Project
presentation
IS5152 Decision Making Technologies
References:
Available in the RBR sections of Central Library and HSS
Business Library.
1.
Neural networks: A comprehensive foundation
Author:
Haykin
, Simon S
2.
Machine Learning
Author: Mitchell, Tom M
3.
Operations research : applications and algorithms
Author: Winston, Wayne L
IS5152 Decision Making Technologies
Grading:
1
.
Continual
assessment
(
50
%
)
:
•
Midterm
Exam
(
20
%
)
•
Class
project
(
30
%
)
:
o
20
%
for
the
project
work,
and
o
10
%
for
project
report
and
presentation
.
o
Projects
are
to
be
carried
out
in
teams
consisting
n
students
.
2
.
Final
exam
on
6
May
pm
:
50
%
.
Both
midterm
exam
and
final
exam
are
open
-
book
examinations
.
IS5152 Decision Making Technologies
Class project:
-
Identify
an
interesting
problem/topic
to
test
one
or
more
of
the
techniques
for
decision
making
discussed
in
class
.
-
Search/find/collect
relevant
data
.
Use
an
available
software
to
analyze
the
data
.
-
Software
will
be
provided
or
they
can
be
obtained
via
the
internet
.
-
Write
a
(max)
20
page
report
.
-
Present
the
project
in
class
(duration
:
20
minutes)
.
-
More
detailed
instructions
about
the
project
will
be
given
later
in
the
semester
.
IS5152 Decision Making Technologies
IVLE
:
1.
Do
check
IVLE
for
this
course
regularly
for
announcements,
updates,
etc
.
2.
All
lecture
materials
will
be
placed
in
the
workbin
.
3.
Message
from
Students
Against
the
Violation
of
the
Earth
(SAVE)
:
•
the
Office
of
Provost
had
approved
the
submission
of
all
academic
assignments
for
undergraduate
and
graduate
studies
on
double
-
sided
print
or
through
electronic
submission
•
you
are
encouraged
to
print
your
lecture
notes
on
both
sides
on
the
paper
.
If
possible
and
depending
on
the
layout
of
the
notes,
also
encourage
them
to
print
4
to
6
pages
on
a
side
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