| Course Type | Elective Course |
|---|
| Course Level | Doctorate Of Science |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 10 |
|---|
| Name of Lecturer(s) |
|
| Learning Outcomes |
At the end of this course, the students;
- Know and apply evaluation methods
- Know how to design hybrid learning systems
- Know how to apply techniques to specific problems
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | None |
|---|
| Course Description | 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. |
|
| Recommended 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 Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| 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 Work Load | 313 |
| Total Work Load / 30 (h) | 10.43 |
| ECTS Credit of the Course | 10 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
