Special Topics in Industrial Engineering I (IE 580)

2014 Fall
Faculty of Engineering and Natural Sciences
Industrial Engineering(IE)
3
10
Kemal Kılıç kkilic@sabanciuniv.edu,
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English
Doctoral, Master
--
Formal lecture,Interactive lecture,One-to-one tutorial
Interactive,Discussion based learning,Project based learning,Task based learning,Case Study
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Programs\Type Required Core Elective Area Elective
Computer Science and Engineering - With Bachelor's Degree *
Computer Science and Engineering - With Master's Degree *
Computer Science and Engineering - With Thesis *
Cyber Security - With Bachelor's Degree *
Cyber Security - With Master's Degree *
Cyber Security - With Thesis *
Electronics Engineering and Computer Science - With Bachelor's Degree *
Electronics Engineering and Computer Science - With Master's Degree *
Electronics Engineering and Computer Science - With Thesis *
Electronics Engineering - With Bachelor's Degree *
Electronics Engineering - With Master's Degree *
Electronics Engineering - With Thesis *
Energy Technologies and Management-With Thesis *
Industrial Engineering - With Bachelor's Degree *
Industrial Engineering - With Master's Degree *
Industrial Engineering - With Thesis *
Leaders for Industry Biological Sciences and Bioengineering - Non Thesis *
Leaders for Industry Computer Science and Engineering - Non Thesis *
Leaders for Industry Electronics Engineering and Computer Science - Non Thesis *
Leaders for Industry Electronics Engineering - Non Thesis *
Leaders for Industry Industrial Engineering - Non Thesis *
Leaders for Industry Materials Science and Engineering - Non Thesis *
Leaders for Industry Mechatronics Engineering - Non Thesis *
Manufacturing Engineering - Non Thesis *
Manufacturing Engineering - With Bachelor's Degree *
Manufacturing Engineering - With Master's Degree *
Manufacturing Engineering - With Thesis *
Materials Science and Nano Engineering-(Pre:Materials Science and Engineering) *
Materials Science and Nano Engineering-(Pre:Materials Science and Engineering) *
Materials Science and Nano Engineering - With Thesis (Pre.Name: Materials Science and Engineering) *
Mechatronics Engineering - With Bachelor's Degree *
Mechatronics Engineering - With Master's Degree *
Mechatronics Engineering - With Thesis *
Molecular Biology, Genetics and Bioengineering (Prev. Name: Biological Sciences and Bioengineering) *
Molecular Biology, Genetics and Bioengineering-(Prev. Name: Biological Sciences and Bioengineering) *
Molecular Biology,Genetics and Bioengineering-With Thesis (Pre.Name:Biological Sciences and Bioeng.) *
Physics - Non Thesis *
Physics - With Bachelor's Degree
Physics - With Master's Degree

CONTENT

OBJECTIVE

To understand the optimization formulation and derivations of various data mining algorithms which includes various algorithms that are developed for feature selection, feature reduction, regression, clasification, clustering and association rule mining.

LEARNING OUTCOMES

  • list the basic components of a data mining process.
  • model a data mining problem and decide which techniques are suitable for the business objective of the user.
  • understand the optimization formulation of the various data mining problems and derive the techniques that can be used in those problems.
  • correctly apply the steps of various feature selection techniques, supervised and unsupervised learning algorithms and association rule mining algorithm.
  • implement feature selection, supervised and unsupervised learning algorithms, association rule mining techniques with a data mining tool (e.g., WEKA) in order to determine the relations that are hidden in the data.

ASSESSMENT METHODS and CRITERIA

  Percentage (%)
Assignment 25
Term-Paper 55
Presentation 20

RECOMENDED or REQUIRED READINGS

Textbook

1 - Hongbo Du, Data Mining Techniques and Applications, CENGAGE Learning, 2010
2- Stuart Russel and Peter Norvig, Artificial Intelligence, A Modern Approach, Prentice Hall, 1995
Optional Readings

Tayfur Altıok, Performance Analysis of Manufacturing Systems

John A. Buzacott and J. George Shanthikumar, Stochastic Models of Manufacturing Systems

Jingshan Li and Semyon M. Meerkov, Production Systems Engineering