BAHÇEŞEHİR UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
| PHOTOGRAPHY AND VIDEO | |||||
| 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 Fall |
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) | Knowledge of photographic and video media and ability to use basic, intermediate and advanced techniques of these media. | |
| 2) | Ability to understand, analyze and evaluate theories, concepts and uses of photography and video. | |
| 3) | Ability to employ theoretical knowledge in the areas of the use of photography and video. | |
| 4) | Familiarity with and ability to review the historical literature in theoretical and practical studies in photography and video. | |
| 5) | Ability in problem solving in relation to projects in photography and video. | |
| 6) | Ability to generate innovative responses to particular and novel requirements in photography and video. | |
| 7) | Understanding and appreciation of the roles and potentials of the image across visual culture | |
| 8) | Ability to communicate distinctively by means of photographic and video images. | |
| 9) | Experience of image post-production processes and ability to develop creative outcomes through this knowledge. | |
| 10) | Knowledge of and ability to participate in the processes of production, distribution and use of photography and video in the media. | |
| 11) | Ability to understand, analyze and evaluate global, regional and local problematics in visual culture. | |
| 12) | Knowledge of and ability to make a significant contribution to the goals of public communication. | |
| 13) | Enhancing creativity via interdisciplinary methods to develop skills for realizing projects. | |
| 14) | Gaining general knowledge about the points of intersection of communication, art and technology. |