Information Systems Engineering | |||||
Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 |
Course Code: | FET313 | ||||||||
Ders İsmi: | Machine Learning | ||||||||
Ders Yarıyılı: | Spring | ||||||||
Ders Kredileri: |
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Language of instruction: | Turkish | ||||||||
Ders Koşulu: | |||||||||
Ders İş Deneyimini Gerektiriyor mu?: | No | ||||||||
Type of course: | Bölüm Seçmeli | ||||||||
Course Level: |
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Mode of Delivery: | E-Learning | ||||||||
Course Coordinator : | Asst. Prof. Dr. ARiF YELĞİ | ||||||||
Course Lecturer(s): |
Asst. Prof. Dr. KÜBRA EROĞLU |
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Course Assistants: |
Course Objectives: | This course covers machine learning algorithms both theoretically and practically on real data. At the end of the course, students are expected to learn the basic concepts in machine learning and apply machine learning algorithms on data, establish a relationship between learning models and engineering applications, and actively participate in the courses throughout the semester. |
Course Content: | First look at machine learning and basic concepts of machine learning, learning theory and its types, Bayesian learning and decision trees, artificial neural networks and genetic algorithms, unsupervised learning and reinforcement learning and ethics in machine learning. |
The students who have succeeded in this course;
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Week | Subject | Related Preparation |
1) | Introduction to Machine Learning | Repetition of previous topics |
2) | Basic Concepts in Machine Learning | Repetition of previous topics |
3) | Learning Theory and Types of Learning | Repetition of previous topics |
4) | Bayesian Learning | Repetition of previous topics |
5) | Decision Tree Learning | Repetition of previous topics |
6) | Artificial neural networks | Repetition of previous topics |
7) | Multilayer Artificial Neural Networks | Repetition of previous topics |
8) | MIDTERM | Repetition of previous topics |
9) | Genetic Algorithms | Repetition of previous topics |
10) | Example-Based Learning | Repetition of previous topics |
11) | Unsupervised Learning | Repetition of previous topics |
12) | Kohonen Networks | Repetition of previous topics |
13) | Supportive Learning | Repetition of previous topics |
14) | Privacy in Machine Learning | Repetition of previous topics |
15) | Privacy in Machine Learning | Repetition of previous topics |
16) | Final Exam | Repetition of previous topics |
Course Notes / Textbooks: | |
References: | Ethem ALPAYDIN, Introduction to Machine Learning, The MIT Press, second edition, 2010. Tom Mitchell, Machine Learning, McGraw-Hill. |
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