INFORMATION TECHNOLOGIES (TURKISH, 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
ISM5206 Decision Analysis Fall
Spring
3 0 3 12
The course opens with the approval of the Department at the beginning of each semester

Basic information

Language of instruction: Tr
Type of course: Departmental Elective
Course Level:
Mode of Delivery: Face to face
Course Coordinator : Instructor ÖZLEM KANGA
Course Lecturer(s): Assoc. Prof. SEROL BULKAN
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 Outputs

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 exam
10) Learning Bayesian networks
11) Influence and decision networks
12) Influence and decision networks
13) Value of information
14) Project presentations

Sources

Course Notes: 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
Attendance % 0
Laboratory % 0
Application % 0
Field Work % 0
Special Course Internship (Work Placement) % 0
Quizzes % 0
Homework Assignments 4 % 10
Presentation % 0
Project 1 % 20
Seminar % 0
Midterms 1 % 30
Preliminary Jury % 0
Final 1 % 40
Paper Submission % 0
Jury % 0
Bütünleme % 0
Total % 100
PERCENTAGE OF SEMESTER WORK % 40
PERCENTAGE OF FINAL WORK % 60
Total % 100

ECTS / Workload Table

Activities Number of Activities Duration (Hours) Workload
Course Hours 14 3 42
Laboratory 0 0 0
Application 0 0 0
Special Course Internship (Work Placement) 0 0 0
Field Work 0 0 0
Study Hours Out of Class 14 2 28
Presentations / Seminar 1 10 10
Project 1 40 40
Homework Assignments 4 10 40
Quizzes 0 0 0
Preliminary Jury 0
Midterms 1 15 15
Paper Submission 0
Jury 0
Final 1 20 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) Uses basic Software Engineering knowledge and competencies.
2) Applies the software development ability that is necessary for software engineering applications.
3) Uses data structures and applies information about algorithm development.
4) Develops system programs on operating systems.
5) Develops system programs on operating systems.
6) Creates the structure of computer networks and network security.
7) Uses business intelligence, data mining and data analysis tools, applies techniques about them.
8) Develops database applications and WEB based programs.
9) Defines, analyzes, designs and manages information technologies projects.
10) Uses and develops technology-based environments and tools in education.
11) Detects, identifies and solves information technology needs of the business environment.
12) Uses the capabilities of information technologies within the rules of professional responsibility and ethics.