National Taiwan University of Science and Technology

reformcartloadΤεχνίτη Νοημοσύνη και Ρομποτική

15 Οκτ 2013 (πριν από 3 χρόνια και 11 μήνες)

86 εμφανίσεις

JSChou


10/15/2013

Syllabus
-
AI FOR PM

1
/
3

National Taiwan University of Science and Technology

Department of Construction Engineering


Management Division

Artificial Intelligence for Project Management

(Course Plan

Subject to Change
)



INSTRUCTOR:

Dr. J.
-
S. Chou


COURSE:

Artificial Intelligence f
or Project Management


OFFICE:

T2
-
223
-
4 /
E2
-
217

E
-
MAIL:

jschou@
mail.ntust
.edu.tw


PHONE:

2737
-
6321

C
OURSE WEBSITE:
http://elearning.ntust.edu.tw/index.html





OFFICE HOUR
:

by appointment

via phon
e or email
, or just come by my office. If I
am
not available, I will set another time for both of us.


COURSE OBJECTIVES:

We are currently living in an era of information explosion. There is a need to extract
intelligent information and discover
useful
k
nowledge in the drowning data through various
techniques. Many companies gathering huge amounts of electronic data have now begun
applying data mining
/
a
rtificial intelligence (AI)

techniques to their data warehouses to
discover and extract pieces of infor
mation useful for making smart business
/management

decisions.
I
nstead of developing
complex
algorithm
s,

t
his course will
explore

some
prevailing

AI

&
mining methods, and
introduce

popular
data mining
software,

and statistical
tools to
present

implicit kno
wledge
in
terms

of visualization, pattern
evaluation/
recognition
and interesting rules. Furthermore, to help the students develop an understanding of when
and how to use each techniques
for
management

problems

or related domain knowledge
applications.


CO
URSE OUTLINE

(
teaching

contents vary and

depend on the time
available

in the
semester
)
:

1.

Introduction t
o Project Management & Artificial Intelligence

2.

Data Preprocessing

from DBMS/DW
: Data handling & dimension reduction
methods

3.

Structural Equation Modeling

f
or Social Science Data

o

EFA and CFA

o

SEM

4.

Data Mining Techniques for Engineering Data

(Prediction, Classification,
Clustering & Association)

o

MRA

o

ANNs

o

CBR

JSChou


10/15/2013

Syllabus
-
AI FOR PM

2
/
3

o

GA

o

DT

o

LR

o

SVM

o

More modern techniques?

5.

Model Evaluation Techniques

6.

Term Project Presenta
tion


PRIMARY TEXTB
OOKS:

1.

Daniel T. Larose (2005),
Discovering Knowledge in Data: An Introduction to D
ata
Mining
, Wiley.

2.

Daniel T. Larose (2006),
Data Mining Methods and Models
, Wiley
.

3.

Hair et al. (20
10
),
Multivariate Data Analysis
,
7
e, Pearson Education International.

4.

Clemen
tine User Manual, Algorithm, and Applications Guidebooks

(2013)
.

5.

Lecture
Handouts

and
Articles

posted on website

6.

Latest

journal papers


T
RIAL SOFTWARE
:

1.

IBM
SPSS
Modeler (
Clementine
)

2.

WEKA

(Open Source Software)

3.

XLMINER

4.

LISREL STUDENT VERSION

or SPSS AMOS

5.

@R
isk,
Evolver, RiskOpt, NeuralTools, StatTools


RECOMMENDED TEXTBOOKS:

1.

Introduction to Data Mining and Knowledge Discovery
,

Third
Edition
,
ISBN:
1
-
892095
-
02
-
5

(Can be downloaded via website)

2.

Tan, P., Steinbach, M., and Kumar, V. (200
6
)
Introduction to Data
Mining
, 1st
edition, Addison
-
Wesley, ISBN: 0
-
321
-
32136
-
7.

3.

H. Witten and E. Frank (200
5
),
Data Mining
:

Practical Machine Learning Tools and
Techniques
, 2nd edition, Morgan Kaufmann, ISBN: 0
-
12
-
088407
-
0
, closely tied to
the WEKA software
.

4.

Ethem ALPAYDIN
,
Introduction to Machine Learning
,
The MIT Press, October
2004, ISBN 0
-
262
-
01211
-
1

5.

J. Han and M. Kamber (
2000)
Data Mining: Concepts and Techniques
, Morgan
Kaufmann
.

6.

Algright et al. (2006),
Data Analysis & Decision Making With Microsoft Excel
,

3e,
Thomson.


GRADING:



Homework











15%

JSChou


10/15/2013

Syllabus
-
AI FOR PM

3
/
3


Journal Ar
ticle
s

Presentation








20%


Pre
-
Proposal Presentation








20%



Team Project

(Presentation + Term Paper)





40%


Attendance












5%


Extra Credits (class discussion, learning attitude, etc.)



5%~10%




Note: Write out your full Student ID, Name, and Assignment in the headline as you
send me the

ass
ignment

email
s

or your mails will be excluded by my automatic
screening

tool.