| INFORMATION TECHNOLOGIES (ENGLISH, NONTHESIS) | |||||
| Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 | ||
| 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. |
| 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. |
|
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. |
| 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 |
| 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 |
| 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 |
| 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 | |
| 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 | |
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution |