AMERICAN CULTURE AND LITERATURE
Bachelor TR-NQF-HE: Level 6 QF-EHEA: First Cycle EQF-LLL: Level 6

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
ISM5206 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: Turkish
Type of course: Non-Departmental Elective
Course Level: Bachelor’s Degree (First Cycle)
Mode of Delivery: Face to face
Course Coordinator : Instructor ÖZLEM KANGA
Course Lecturer(s): Assoc. Prof. SEROL BULKAN
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 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 / 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 Duration (Hours) Workload
Course Hours 14 3 42
Study Hours Out of Class 14 2 28
Presentations / Seminar 1 10 10
Project 1 40 40
Homework Assignments 4 10 40
Midterms 1 15 15
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) Upon graduation, students will acquire key skills and attributes to conduct research to use research tools, to solve problems, to communicate effectively and to transfer skills to the workplace.
2) Upon graduation, students will have developed the ability to discuss key issues in fluent English.
3) Upon graduation, students will have developed the ability to compose written documents in English with a mature prose style. 4
4) Upon graduation, students will have gained broad knowledge of the American and English literary canons. 4
5) Upon graduation, students will have developed the ability to analyze, synthesize and criticize sophisticated works of American and English literature. 4
6) Upon graduation, students will have achieved in depth the understanding of contemporary American culture. 3
7) Upon graduation, students will have developed the ability to draw links among diverse literary texts and documents and establish critical connections and adopt an interdisciplinary attitude. 3
8) Upon graduation, students will be able to develop new projects individually or in teams. 3
9) Upon graduation, students will be able to apply their knowledge into their lives for interdisciplinary problem-solving and solutions. 4