COMPUTER ENGINEERING | |||||
Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 |
Course Code: | 1410002007 | ||||||||
Ders İsmi: | Introduction to Machine Learning | ||||||||
Ders Yarıyılı: |
Fall Spring |
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Ders Kredileri: |
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Language of instruction: | TR | ||||||||
Ders Koşulu: | |||||||||
Ders İş Deneyimini Gerektiriyor mu?: | No | ||||||||
Type of course: | Bölüm Seçmeli | ||||||||
Course Level: |
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Mode of Delivery: | Face to face | ||||||||
Course Coordinator : | Dr.Öğr.Üyesi Recep DURANAY | ||||||||
Course Lecturer(s): |
Prof. Dr. Haluk GÜMÜŞKAYA |
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Course Assistants: |
Course Objectives: | The course aims to provide students with information about the basic methods and approaches in the field of machine learning and to help students gain the ability to apply machine learning methods to practical problems. |
Course Content: | Basic concepts and approaches of machine learning. Guided machine learning methods. Concept learning and learning with decision trees. Bayes' theorem-based approaches in machine learning. Evolutionary approach and genetic programming. Learning and reinforcement learning with artificial neural networks, support vectors. Undirected learning methods and classification. |
The students who have succeeded in this course;
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Week | Subject | Related Preparation |
1) | Introduction to Machine Learning | Course Book |
2) | Concept Learning | Course Book |
3) | Learning with Decision Trees | Course Book |
4) | Genetic Algorithms and Genetic Programming | Course Book |
5) | Project Proposal Submissions | Course Book |
6) | Learning with the Bayesian Approach | Course Book |
7) | Bayesian Belief Networks | Course Book |
8) | Midterm | |
9) | Artificial neural networks | Course Book |
10) | Feedback Neural Networks | Course Book |
11) | Learning with Support Vectors | Course Book |
12) | Learning with Reinforcement | Course Book |
13) | Undirected Learning | Course Book |
14) | Project Presentations | Course Book |
15) | Project Presentations | Course Book |
16) | Final |
Course Notes / Textbooks: | Machine Learning, McGraw-Hill, T. Mitchell (1997) |
References: | Machine Learning, McGraw-Hill, T. Mitchell (1997) |
Ders Öğrenme Kazanımları | 1 |
2 |
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Program Outcomes | ||||||||||||||||
1) PO 1.1) Sufficient knowledge in mathematics, science and computer engineering | ||||||||||||||||
2) PO 1.2) Ability to apply theoretical and applied knowledge in mathematics, science and computer engineering for modeling and solving engineering problems. | ||||||||||||||||
3) PO 2.1) Identifying complex engineering problems | ||||||||||||||||
4) PO 2.2) Defining complex engineering problems | ||||||||||||||||
5) PO 2.3) Formulating complex engineering problems | ||||||||||||||||
6) PO 2.4) Ability to solve complex engineering problems | ||||||||||||||||
7) PO 2.5) Ability to choose and apply appropriate analysis and modeling methods | ||||||||||||||||
8) PO 3.1) Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions. | ||||||||||||||||
9) PO 3.2) Ability to apply modern design methods under realistic constraints and conditions for a complex system, process, device or product | ||||||||||||||||
10) PO 4.1) Developing modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications | ||||||||||||||||
11) PO 4.2) Ability to select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications | ||||||||||||||||
12) PO 4.3) Ability to use information technologies effectively. | ||||||||||||||||
13) PO 5.1) Examination of complex engineering problems or discipline-specific research topics, designing experiments | ||||||||||||||||
14) PO 5.2) Examination of complex engineering problems or discipline-specific research topics, experimentation | ||||||||||||||||
15) PO 5.3 ) Analysis of complex engineering problems or discipline-specific research topics, data collection | ||||||||||||||||
16) PO 5.4) Analyzing the results of complex engineering problems or discipline-specific research topics | ||||||||||||||||
17) PO 5.5) Examining and interpreting complex engineering problems or discipline-specific research topics |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution | |
1) | PO 1.1) Sufficient knowledge in mathematics, science and computer engineering | |
2) | PO 1.2) Ability to apply theoretical and applied knowledge in mathematics, science and computer engineering for modeling and solving engineering problems. | |
3) | PO 2.1) Identifying complex engineering problems | |
4) | PO 2.2) Defining complex engineering problems | |
5) | PO 2.3) Formulating complex engineering problems | 5 |
6) | PO 2.4) Ability to solve complex engineering problems | |
7) | PO 2.5) Ability to choose and apply appropriate analysis and modeling methods | |
8) | PO 3.1) Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions. | |
9) | PO 3.2) Ability to apply modern design methods under realistic constraints and conditions for a complex system, process, device or product | |
10) | PO 4.1) Developing modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications | |
11) | PO 4.2) Ability to select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications | |
12) | PO 4.3) Ability to use information technologies effectively. | |
13) | PO 5.1) Examination of complex engineering problems or discipline-specific research topics, designing experiments | |
14) | PO 5.2) Examination of complex engineering problems or discipline-specific research topics, experimentation | |
15) | PO 5.3 ) Analysis of complex engineering problems or discipline-specific research topics, data collection | |
16) | PO 5.4) Analyzing the results of complex engineering problems or discipline-specific research topics | |
17) | PO 5.5) Examining and interpreting complex engineering problems or discipline-specific research topics |
Yazılı Sınav (Açık uçlu sorular, çoktan seçmeli, doğru yanlış, eşleştirme, boşluk doldurma, sıralama) | |
Homework |
Semester Requirements | Number of Activities | Level of Contribution |
Attendance | 10 | % 10 |
Presentation | 1 | % 20 |
Midterms | 1 | % 30 |
Semester Final Exam | 1 | % 40 |
total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 60 | |
PERCENTAGE OF FINAL WORK | % 40 | |
total | % 100 |
Activities | Number of Activities | Duration (Hours) | Workload |
Course Hours | 14 | 3 | 42 |
Study Hours Out of Class | 35 | 0 | 0 |
Midterms | 1 | 2 | 2 |
Final | 1 | 3 | 3 |
Total Workload | 47 |