INE5206 Decision AnalysisBahçeşehir UniversityDegree Programs INDUSTRIAL ENGINEERING (ENGLISH, THESIS)General Information For StudentsDiploma SupplementErasmus Policy StatementNational Qualifications
INDUSTRIAL ENGINEERING (ENGLISH, THESIS)
Master TR-NQF-HE: Level 7 QF-EHEA: Second Cycle EQF-LLL: Level 7

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
INE5206 Decision Analysis Spring 3 0 3 12
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction: English
Type of course: Departmental Elective
Course Level:
Mode of Delivery: Face to face
Course Coordinator :
Recommended Optional Program Components: N.A.
Course Objectives: The aim of the course is to introduce the graphical models used in decision analysis and to provide a set of systematic tools to help the decision maker in giving a decision.

Learning Outcomes

The students who have succeeded in this course;
- Recognize the graphical models used in decision analysis.
- Model a given uncertain situation with Bayes networks.
- Compute exact and approximate inferences in Bayes networks.
- Model a given uncertain decision problem with influence diagrams.
- Make inferences in decision networks.
- Compute value of information.

Course Content

Expected Utility, Causal and Bayesian networks, Exact inference in Bayesian networks, Approximate inference in Bayesian networks, Learning Bayesian networks, Influence and decision networks, Value of information

Weekly Detailed Course Contents

Week Subject Related Preparation
1) Probability review
2) Expected Utility
3) Causal and Bayesian networks
4) Building Bayesian models
5) Exact inference in Bayesian networks
6) Exact inference in Bayesian networks
7) Approximate inference in Bayesian networks
8) Approximate inference in Bayesian networks
9) Midterm
10) Learning Bayesian networks
11) Influence and decision networks
12) Influence and decision networks
13) Value of information
14) Project presentations

Sources

Course Notes / Textbooks: F.V. Jensen, 2001. Bayesian networks and decision graphs, New York : Springer
References: Robert T. Clemen, 1996. Making Hard Decisions: An Introduction to Decision Analysis, 2nd edition, Duxbury Press

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Homework Assignments 4 % 10
Project 1 % 20
Midterms 1 % 30
Final 1 % 40
Total % 100
PERCENTAGE OF SEMESTER WORK % 40
PERCENTAGE OF FINAL WORK % 60
Total % 100

ECTS / Workload Table

Activities Number of Activities Workload
Course Hours 14 42
Study Hours Out of Class 14 28
Presentations / Seminar 1 10
Project 4 40
Homework Assignments 4 40
Midterms 1 15
Final 1 20
Total Workload 195

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution
1) Process view and analytic thinking 3
2) managerial thinking with technical background 3
3) To have theoretical knowledge on operations research. 3
4) Awareness about the applications of operations research 4
5) To have ability of selection and efficient use of modern techniques, equipments and information technologies for industrial engineering 2
6) To be capable of designing and conducting experiments and collecting data, analyzing and interpreting results
7) To have verbal and oral effective communication skills by using visual methods in Turkish and English 2
8) To be aware of entrepreneurship, sustainability and innovation
9) To lead disciplinary and multi-disciplinary teams, to develop solution approaches in complex situations, to work individually and to take responsibility. 1
10) To have conscious of professional and ethical responsibility
11) To have conscious of necessity to lifelong learning
12) To be aware of economic and legal implications of engineering solutions
13) Economic, social and environmental responsibility while solving management problems