Information Package / Course Catalogue
| Computer Engineering | |||||
| Bachelor | Length of the Programme: 4 | Number of Credits: 240 | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF: Level 6 |
Ders Genel Tanıtım Bilgileri
| School/Faculty/Institute | Faculty of Engineering | |||||
| Course Code | COMP 462 | |||||
| Course Title in English | Introduction to Machine Learning | |||||
| Course Title in Turkish | Yapay Öğrenmeye Giriş | |||||
| Language of Instruction | EN | |||||
| Type of Course | Flipped Classroom | |||||
| Level of Course | Introductory | |||||
| Semester | Fall | |||||
| Contact Hours per Week |
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| Estimated Student Workload | 152 hours per semester | |||||
| Number of Credits | 6 ECTS | |||||
| Grading Mode | Standard Letter Grade | |||||
| Pre-requisites | None | |||||
| Expected Prior Knowledge | Prior knowledge in programming, probability and statistics. | |||||
| Co-requisites | None | |||||
| Registration Restrictions | Only Undergraduate 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: Supervised learning, unsupervised learning, regression methods, outlier detection, feature analysis, validation and evaluation. | |||||
| Course Description in Turkish | Bu ders yapay öğrenmede kullanılan temel yöntemleri içermektedir: Gözetimli ve gözetimsiz öğrenme, bağlanım yöntemleri, aykırılık tespiti, öznitelik analizi, geçerleme ve performans değerlendirmesi |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) identify and solve a complex engineering problem using machine learning techniques; 2) design a machine learning system to produce solutions; 3) present the results of a machine learning solution to a range of audiences; 4) recognize ethical and professional responsibilities in creating the machine learning system; 5) analyze and interpret the data used for the machine learning system; 6) acquire and apply new knowledge of machine learning techniques; |
| Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|
| 1) An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics | ||||||
| 2) An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors | ||||||
| 3) An ability to communicate effectively with a range of audiences | ||||||
| 4) An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts | ||||||
| 5) An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives | ||||||
| 6) An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions | ||||||
| 7) An ability to acquire and apply new knowledge as needed, using appropriate learning strategies. |
Relation to Program Outcomes and Competences
| N None | S Supportive | H Highly Related |
| Program Outcomes and Competences | Level | Assessed by | |
| 1) | An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics | H | Exam,HW,Project |
| 2) | An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors | H | Exam,HW,Project |
| 3) | An ability to communicate effectively with a range of audiences | S | HW,Project |
| 4) | An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts | S | HW,Project |
| 5) | An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives | N | |
| 6) | An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions | S | HW,Project |
| 7) | An ability to acquire and apply new knowledge as needed, using appropriate learning strategies. | S | HW,Project |
| Prepared by and Date | YASSINE DRIAS , February 2023 |
| Course Coordinator | YASSINE DRIAS |
| Semester | Fall |
| Name of Instructor | Asst. Prof. Dr. TUNA ÇAKAR |
Course Contents
| Week | Subject |
| 1) | Introduction to Machine Learning Concepts |
| 2) | K Nearest Neighbor Algorithm |
| 3) | Training, Testing and Validating Machine Learning Systems |
| 4) | Clustering Techniques |
| 5) | Decision Trees (Part 1) |
| 6) | Decision Trees (Part 2) |
| 7) | Gradient Descent Algorithm and Linear Regression |
| 8) | Logistic Regression |
| 9) | Feature Selection and Extraction Techniques |
| 10) | Advanced Classification Methods |
| 11) | Artificial Neural Networks (Part 1) |
| 12) | Artificial Neural Networks (Part 2) |
| 13) | Deep Learning Methods (Part 1) |
| 14) | Deep Learning Methods (Part 2) |
| 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 |
driasy@mef.edu.tr Instructor’s office: 5th floor Phone number: 0 212 395 37 45 Office hours: After the lecture hours. E-mail address: driasy@mef.edu.tr Rules for attendance: No attendance required. Statement on plagiarism: YÖK Regulations http://3fcampus.mef.edu.tr/uploads/cms/webadmin.mef.edu.tr/4833_2.pdf |
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ECTS Student Workload Estimation
| 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 | 1 | 3 | 1 | 70 | ||
| Homework Assignments | 4 | 1 | 14 | 2 | 68 | ||
| Midterm(s) | 1 | 10 | 2 | 2 | 14 | ||
| Total Workload | 152 | ||||||
| Total Workload/25 | 6.1 | ||||||
| ECTS | 6 | ||||||