| Course Type | Elective Course |
|---|
| Course Level | Master's Degree With Thesis |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 10 |
|---|
| Name of Lecturer(s) |
|
| Learning Outcomes |
At the end of this course, the students;
- Know hypotheses and version spaces.
- Know how to design a learning system.
- Know how to apply machine learning techniques to specific problems.
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | None |
|---|
| Course Description | Machine learning paradigms. Concept learning, general-to-specific ordering and version spaces. Decision tree learning. Artificial neural networks, perceptrons, and multilayer networks. Bayesian learning. Instance-based learning. Support vector machine. Genetic algorithms. Applications to selected problems. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Machine learning paradigms. |
|
2nd Week |
Concept learning, general-to-specific ordering and version spaces. |
|
3rd Week |
Concept learning, general-to-specific ordering and version spaces. |
|
4th Week |
Decision tree learning. |
|
5th Week |
Decision tree learning. |
|
6th Week |
Artificial neural networks, perceptrons, and multilayer networks. |
|
7th Week |
Artificial neural networks, perceptrons, and multilayer networks. |
|
8th Week |
Mid-term |
|
9th Week |
Bayesian learning. |
|
10th Week |
Instance-based learning. |
|
11th Week |
Support vector machine. |
|
12th Week |
Genetic algorithms. |
|
13th Week |
Genetic algorithms. |
|
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 |
| Quiz |
1 |
15 |
| Project |
1 |
15 |
| 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 |
1 |
1 |
| Preparation for Quiz |
|
|
|
| Individual or group work |
|
|
|
| Preparation for Final exam |
1 |
30 |
30 |
| Course hours |
14 |
3 |
42 |
| Preparation for Midterm exam |
1 |
20 |
20 |
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
2 |
2 |
| Homework |
2 |
30 |
60 |
| Project |
2 |
70 |
140 |
| Total Work Load | 295 |
| Total Work Load / 30 (h) | 9.83 |
| ECTS Credit of the Course | 10 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
