- Course Code
- RYZ201
- Semester
- Third Term (Fall)
- Theory+Practice
- 2 + 1
- ECTS
- 6
| Course Type | Compulsory Course |
|---|
| Course Level | Associate Degree |
|---|
| Year of Study | 2 |
|---|
| Semester | Third Term (Fall) |
|---|
| ECTS | 6 |
|---|
| Name of Lecturer(s) |
- |
| Learning Outcomes |
At the end of this course, the students;
- Understands the basic concepts and algorithms in machine learning.
- Apply data preprocessing techniques to improve model performance.
- Effectively apply supervised techniques for regression and classification.
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | None |
|---|
| Course Description | To teach basic machine learning concepts and algorithms such as data preprocessing, supervised and unsupervised learning techniques, evaluation criteria, and model selection. It covers basic machine learning concepts and algorithms, such as data preprocessing, supervised learning (including regression and classification), unsupervised learning (clustering and size reduction), evaluation criteria, and model selection. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Introduction to machine learning fundamentals |
|
2nd Week |
Data preprocessing techniques and feature |
|
3rd Week |
Supervised learning: regression techniques |
|
4th Week |
Supervised learning: classification methods |
|
5th Week |
Unsupervised learning: clustering approaches |
|
6th Week |
Unsupervised learning: methods of size reduction |
|
7th Week |
Model evaluation and selection metrics |
|
8th Week |
Midterm Exam |
|
9th Week |
Introduction to deep learning concepts |
|
10th Week |
Case studies: applying machine learning in real-world scenarios |
|
11th Week |
Practical applications: implementation of machine learning models |
|
12th Week |
Advanced applications of machine learning |
|
13th Week |
Study of machine learning in industry and research |
|
14th Week |
Challenges and ethics in machine learning |
|
| Recommended or Required Reading | ***If you feel the need for academic accommodations due to your disability, please contact the instructor of the course.
|
|---|
| Planned Learning Activities and Teaching Methods |
- Anlatım
- Soru-Cevap
- Sorun/Problem Çözme
- Eğitim-Uygulama
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
40 |
| Total(%) |
|
40 |
| Contribution of In-term Studies to Overall Grade(%) |
|
40 |
| Contribution of Final Examination to Overall Grade(%) |
|
60 |
| 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 |
14 |
5 |
70 |
| Preparation for Final exam |
1 |
14 |
14 |
| Course hours |
14 |
3 |
42 |
| Preparation for Midterm exam |
1 |
7 |
7 |
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
1 |
1 |
| Homework |
|
|
|
| Total Work Load | 135 |
| Total Work Load / 30 (h) | 4.5 |
| ECTS Credit of the Course | 6 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
Programme Qualifications / Learning Outcomes
|
LO1 |
LO2 |
LO3 |
| K1 |
X |
X |
X |
| K2 |
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| K3 |
X |
X |
X |
| K4 |
X |
X |
X |
| K5 |
|
|
|
| K6 |
X |
X |
X |
| K7 |
|
|
|
| K8 |
|
|
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| K9 |
|
|
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| K10 |
|
|
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| K11 |
|
|
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| K12 |
|
|
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| K13 |
|
|
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| K14 |
X |
X |
X |
| K15 |
|
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| K16 |
|
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