| Course Objectives: |
The aim of this course is to introduce students to fundamental machine learning concepts, algorithms, and application areas, and to teach them supervised, unsupervised, and semi-supervised learning techniques that they can use in data analysis. The goal is to enable students to develop machine learning models on simple datasets, evaluate these models, and interpret application results. |
| Course Content: |
The fundamental concepts, algorithms, and application areas of machine learning are covered. Supervised, unsupervised, and semi-supervised learning methods are discussed, explaining how these techniques can be used in various data analysis processes. The course covers basic classification, regression, and clustering methods, model development, evaluation criteria, and results interpretation, all demonstrated through practical examples. Students gain the ability to build machine learning models on simple datasets, measure their performance, and analyze the resulting output. |
Course Learning Outcomes (CLOs) are those describing the knowledge, skills and competencies that students are expected to achieve upon successful completion of the course. In this context, Course Learning Outcomes defined for this course unit are as follows:
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| Knowledge
(Described as Theoritical and/or Factual Knowledge.)
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1) Explains the basic concepts and types of algorithms in machine learning.
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2) Distinguish between supervised and unsupervised learning methods and determine their appropriate areas of use.
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| Skills
(Describe as Cognitive and/or Practical Skills.)
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1) Applies basic machine learning algorithms on simple datasets (e.g.: k-NN, decision trees, k-means).
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2) Applies data preprocessing techniques (cleaning, normalization, feature selection, etc.).
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3) Develops basic machine learning applications using Python or a similar programming language.
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| Competences
(Described as "Ability of the learner to apply knowledge and skills autonomously with responsibility", "Learning to learn"," Communication and social" and "Field specific" competences.)
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1) It uses performance metrics such as accuracy and error rate to evaluate model performance.
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| Week |
Subject |
Materials Sharing * |
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Related Preparation |
Further Study |
| 1) |
Introduction to Machine Learning-I |
Sorhun, E. (2021). Machine Learning with Python. Abakus Publishing.
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| 2) |
Introduction to Machine Learning-II |
Sorhun, E. (2021). Machine Learning with Python. Abakus Publishing.
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| 3) |
Supervised Learning |
Sorhun, E. (2021). Machine Learning with Python. Abakus Publishing.
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| 4) |
Supervised Learning Application |
Sorhun, E. (2021). Machine Learning with Python. Abakus Publishing.
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| 5) |
Supervised Learning Application |
Sorhun, E. (2021). Machine Learning with Python. Abakus Publishing.
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| 6) |
Unsupervised Learning |
Sorhun, E. (2021). Machine Learning with Python. Abakus Publishing.
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| 7) |
Unsupervised Learning Application |
Sorhun, E. (2021). Machine Learning with Python. Abakus Publishing.
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| 8) |
Midterm |
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| 9) |
Unsupervised Learning Application |
Sorhun, E. (2021). Machine Learning with Python. Abakus Publishing.
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| 10) |
Semi-Supervised Learning |
Sorhun, E. (2021). Machine Learning with Python. Abakus Publishing.
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| 11) |
Semi-Supervised Learning Application |
Sorhun, E. (2021). Machine Learning with Python. Abakus Publishing.
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| 12) |
Semi-Supervised Learning Application |
Sorhun, E. (2021). Machine Learning with Python. Abakus Publishing.
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| 13) |
Machine Learning Course Project-I |
Sorhun, E. (2021). Machine Learning with Python. Abakus Publishing.
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| 14) |
Machine Learning Course Project-II |
Sorhun, E. (2021). Machine Learning with Python. Abakus Publishing.
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Programme Learning Outcomes |
Contribution Level (from 1 to 5) |
| 1) |
It explains fundamental concepts in mathematics, statistics, and probability; and applies this knowledge to data analysis, modeling, and interpretation of results. |
1 |
| 2) |
It explains the principles of algorithm design and develops software for solving problems using at least one programming language. |
3 |
| 3) |
It compares machine learning and data mining algorithms, selects the appropriate method, and applies it to real data. |
5 |
| 4) |
Big data platforms utilize distributed systems and cloud computing architectures to perform data processing operations. |
1 |
| 5) |
They apply natural language processing techniques to text data and develop basic NLP-based applications. |
2 |
| 6) |
It analyzes different data sources, transforms them into meaningful outputs, and presents them using appropriate visualization tools. |
2 |
| 7) |
It creates data-driven decision models using decision support systems. |
1 |
| 8) |
It develops optimization models and produces solutions for industrial and sectoral problems. |
1 |
| 9) |
In professional practice, we operate within the framework of ethical principles, data security, and social responsibility. |
4 |
| 10) |
They keep up with current technological developments in their field, actively participate in teamwork, and develop a lifelong learning awareness. |
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| WORKLOAD OF TEACHING & LEARNING ACTIVITIES |
| Teaching & Learning Activities |
# of Activities per semester |
Duration (hour) |
Total Workload |
| Course |
14 |
3 |
42 |
| Laboratory |
0 |
0 |
0 |
| Application |
0 |
0 |
0 |
| Special Course Internship (Work Placement) |
0 |
0 |
0 |
| Field Work |
0 |
0 |
0 |
| Study Hours Out of Class |
14 |
1.5 |
21 |
| Presentations / Seminar |
0 |
0 |
0 |
| Project |
1 |
16 |
16 |
| Homework Assignments |
0 |
0 |
0 |
| Total Workload of Teaching & Learning Activities |
- |
- |
79 |
| WORKLOAD OF ASSESMENT & EVALUATION ACTIVITIES |
| Assesment & Evaluation Activities |
# of Activities per semester |
Duration (hour) |
Total Workload |
| Quizzes |
0 |
0 |
0 |
| Midterms |
1 |
30 |
30 |
| Semester Final Exam |
1 |
44 |
44 |
| Total Workload of Assesment & Evaluation Activities |
- |
- |
74 |
| TOTAL WORKLOAD (Teaching & Learning + Assesment & Evaluation Activities) |
153 |
| ECTS CREDITS OF THE COURSE (Total Workload/25.5 h) |
6 |