| TYPE OF COURSE UNIT | Elective Course |
| LEVEL OF COURSE UNIT | Doctorate Of Science |
| YEAR OF STUDY | - |
| SEMESTER | - |
| NUMBER OF ECTS CREDITS ALLOCATED | 10 |
| NAME OF LECTURER(S) | Associate Professor Mustafa Sert
|
| LEARNING OUTCOMES OF THE COURSE UNIT |
At the end of this course, the students;
1) Know and apply evaluation methods
2) Know how to design hybrid learning systems
3) Know how to apply techniques to specific problems
|
| MODE OF DELIVERY | Face to face |
| PRE-REQUISITES OF THE COURSE | No |
| RECOMMENDED OPTIONAL PROGRAMME COMPONENT | None |
| COURSE DEFINITION | Machine learning paradigms. Evaluation methods for machine learning. Reinforcement learning. Cost-sensitive learning. Analytical learning. Combining inductive and analytical learning methods. Active learning. Applications to selected problems. |
| COURSE CONTENTS | | WEEK | TOPICS |
|---|
| 1st Week | Machine learning paradigms. | | 2nd Week | Evaluation methods for machine learning. | | 3rd Week | Reinforcement learning. | | 4th Week | Cost-sensitive learning. | | 5th Week | Analytical learning. | | 6th Week | Combining inductive and analytical learning methods. | | 7th Week | Active learning. | | 8th Week | Mid-term | | 9th Week | Applications to selected problems. | | 10th Week | Applications to selected problems. | | 11th Week | Applications to selected problems. | | 12th Week | Applications to selected problems. | | 13th Week | Applications to selected problems. | | 14th Week | Applications to selected problems. |
|
| RECOMENDED OR REQUIRED READING | 1. Machine Learning, Tom Mitchell, McGraw-Hill.
2. Pattern Recognition and Machine Learning, Christopher Bishop, Springer.
3. Pattern Classification, Richard O. Duda, Wiley.
|
| PLANNED LEARNING ACTIVITIES AND TEACHING METHODS | Lecture,Questions/Answers,Practice,Problem Solving,Project,Report Preparation,Presentation |
| ASSESSMENT METHODS AND CRITERIA | | | Quantity | Percentage(%) |
|---|
| Mid-term | 1 | 30 | | Project | 1 | 30 | | Total(%) | | 60 | | Contribution of In-term Studies to Overall Grade(%) | | 60 | | Contribution of Final Examination to Overall Grade(%) | | 40 | | Total(%) | | 100 |
|
| ECTS WORKLOAD |
| Activities |
Number |
Hours |
Workload |
| Midterm exam | 1 | 2 | 2 | | Preparation for Quiz | | | | | Individual or group work | 14 | 11 | 154 | | Preparation for Final exam | 1 | 69 | 69 | | Course hours | 14 | 3 | 42 | | Preparation for Midterm exam | 1 | 44 | 44 | | Laboratory (including preparation) | | | | | Final exam | 1 | 2 | 2 | | Homework | | | | | Total Workload | | | 313 |
|---|
| Total Workload / 30 | | | 10,43 |
|---|
| ECTS Credits of the Course | | | 10 |
|---|
|
| LANGUAGE OF INSTRUCTION | Turkish |
| WORK PLACEMENT(S) | No |
| | |
