School/Faculty/Institute | Graduate School | ||||
Course Code | ITC 502 | ||||
Course Title in English | Machine Learning and Deep Learning | ||||
Course Title in Turkish | Machine Learning and Deep Learning | ||||
Language of Instruction | EN | ||||
Type of Course | Flipped Classroom | ||||
Level of Course | Intermediate | ||||
Semester | Spring | ||||
Contact Hours per Week |
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Estimated Student Workload | 186 hours per semester | ||||
Number of Credits | 7.5 ECTS | ||||
Grading Mode | Standard Letter Grade | ||||
Pre-requisites | None | ||||
Expected Prior Knowledge | None | ||||
Co-requisites | None | ||||
Registration Restrictions | Only Graduate Students | ||||
Overall Educational Objective | To learn the fundamentals of machine learning methods and how to design and implement intelligent systems to make prediction, classification, and regression. | ||||
Course Description | This course covers the fundamentals of machine learning approaches. Topics include supervised learning, unsupervised learning, classification, and regression methods. | ||||
Course Description in Turkish | Bu ders yapay öğrenmede kullanılan temel yöntemleri içermektedir. Konular, gözetimli ve gözetimsiz öğrenme yöntemleri, sınıflandırma ve regresyon metodlarıdır. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) Apply classification methods to recognize patterns 2) Apply regression methods to estimate unknown functions 3) Assess the performance of machine learning methods 4) Design and construct a machine learning system for a given problem |
Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 |
---|---|---|---|---|
1) An ability to develop and deepen one's knowledge in the field of mechatronics and robotics engineering at the level of expertise based on acquired undergraduate level qualifications. | ||||
2) An ability to acquire scientific and practical knowledge in mechatronics and robotics. | ||||
3) A comprehensive knowledge about analysis and modeling methods in mechatronics and their limitations. | ||||
4) An ability to design and apply analytical, modeling and experimental based research by analyzing and interpreting complex situations encountered in the design process. | ||||
5) An ability to transmit the process and results of the work of mechatronics and robotics systems systematically and clearly in written and oral form in national and international environments. | ||||
6) An ability to recognize social, scientific and ethical values in the stages of designing and realizing mechatronics and robotic systems and in all professional activities. | ||||
7) An ability to follow new and developing practices in the profession and to apply them in their work. | ||||
8) An ability to take leadership in multi-disciplinary teams, taking responsibility in the design and analysis of mechatronics and robotic systems in complex situations. | ||||
9) An ability to communicate verbally and in writing in English at least at the level of B2 of European Language Portfolio. | ||||
10) An understanding of the social and environmental aspects of mechatronics and robotics applications. |
N None | S Supportive | H Highly Related |
Program Outcomes and Competences | Level | Assessed by | |
1) | An ability to develop and deepen one's knowledge in the field of mechatronics and robotics engineering at the level of expertise based on acquired undergraduate level qualifications. | N | |
2) | An ability to acquire scientific and practical knowledge in mechatronics and robotics. | N | |
3) | A comprehensive knowledge about analysis and modeling methods in mechatronics and their limitations. | N | |
4) | An ability to design and apply analytical, modeling and experimental based research by analyzing and interpreting complex situations encountered in the design process. | N | |
5) | An ability to transmit the process and results of the work of mechatronics and robotics systems systematically and clearly in written and oral form in national and international environments. | N | |
6) | An ability to recognize social, scientific and ethical values in the stages of designing and realizing mechatronics and robotic systems and in all professional activities. | N | |
7) | An ability to follow new and developing practices in the profession and to apply them in their work. | N | |
8) | An ability to take leadership in multi-disciplinary teams, taking responsibility in the design and analysis of mechatronics and robotic systems in complex situations. | N | |
9) | An ability to communicate verbally and in writing in English at least at the level of B2 of European Language Portfolio. | N | |
10) | An understanding of the social and environmental aspects of mechatronics and robotics applications. | N |
Prepared by and Date | , |
Course Coordinator | TUNA ÇAKAR |
Semester | Spring |
Name of Instructor | Asst. Prof. Dr. TUNA ÇAKAR |
Week | Subject |
1) | Introduction to Machine Learning Concepts |
2) | Supervised and Unsupervised Methods |
3) | Classification and Regression |
4) | k-Nearest Neighbor |
5) | Decision Trees |
6) | Feature Selection |
7) | Feature Extraction |
8) | Clustering |
9) | Term Project Progress Presentations |
10) | Performance Evaluation: Training, Testing and Validation |
11) | Artificial Neural Networks – Part 1 |
12) | Artificial Neural Networks – Part 1 |
13) | Deep Neural Networks |
14) | Term Project Presentations |
15) | Final Examination Period |
16) | Final Examination Period |
Required/Recommended Readings | Introduction to Machine Learning, Ethem Alpaydın, MIT Press, 3rd Edition (2015) | ||||||||||||
Teaching Methods | Flipped Classroom | ||||||||||||
Homework and Projects | Assignments | ||||||||||||
Laboratory Work | None | ||||||||||||
Computer Use | Required | ||||||||||||
Other Activities | None | ||||||||||||
Assessment Methods |
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Course Administration |
Academic dishonesty and plagiarism will be subject to Law on Higher Education Article 54. |
Activity | No/Weeks | Hours | Calculation | ||||
No/Weeks per Semester | Preparing for the Activity | Spent in the Activity Itself | Completing the Activity Requirements | ||||
Course Hours | 14 | 2 | 3 | 2 | 98 | ||
Project | 4 | 2 | 18 | 2 | 88 | ||
Total Workload | 186 | ||||||
Total Workload/25 | 7.4 | ||||||
ECTS | 7.5 |