Course Detail
Course Description
Course | Code | Semester | T+P (Hour) | Credit | ECTS |
---|
INTRODUCTION to MACHINE LEARNING | COE3167980 | Fall Semester | 3+0 | 3 | 6 |
Prerequisites Courses | |
Recommended Elective Courses | |
Language of Course | English |
Course Level | First Cycle (Bachelor's Degree) |
Course Type | Elective |
Course Coordinator | Prof.Dr. Bahadır Kürşat GÜNTÜRK |
Name of Lecturer(s) | Prof.Dr. Bahadır Kürşat GÜNTÜRK |
Assistant(s) | |
Aim | To be able to apply and evaluate machine learning techniques. |
Course Content | This course contains; Elements of machine learning,Regression,Basics of classification,Bayesian classifier,Logistic regression,Support vector machines,Neural networks,Convolutional neural networks,Decision trees,Ensemble methods,Feature selection,Principal component analysis,Clustering,Model evaluation. |
Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
Applies regression techniques | 12, 14, 16, 6, 9 | A, E |
Evaluates classification techniques | 12, 14, 16, 6, 9 | A, E |
Applies unsupervised machine learning techniques | 12, 14, 16, 6, 9 | A, E |
Applies feature selection / analysis techniques | 12, 14, 16, 6, 9 | A, E |
Teaching Methods: | 12: Problem Solving Method, 14: Self Study Method, 16: Question - Answer Technique, 6: Experiential Learning, 9: Lecture Method |
Assessment Methods: | A: Traditional Written Exam, E: Homework |
Course Outline
Order | Subjects | Preliminary Work |
---|
1 | Elements of machine learning | |
2 | Regression | |
3 | Basics of classification | |
4 | Bayesian classifier | |
5 | Logistic regression | |
6 | Support vector machines | |
7 | Neural networks | |
8 | Convolutional neural networks | |
9 | Decision trees | |
10 | Ensemble methods | |
11 | Feature selection | |
12 | Principal component analysis | |
13 | Clustering | |
14 | Model evaluation | |
Resources |
Bishop, “Pattern Recognition and Machine Learning,” Springer, (1st
edition)
Duda, Hart, and Stork, “Pattern Classification,” Wiley-Interscience, (2nd
edition) |
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications |
No | Program Qualification | Contribution Level |
1 | 2 | 3 | 4 | 5 |
1 | 1. An ability to apply knowledge of mathematics, science, and engineering | | | | X | |
2 | 2. An ability to identify, formulate, and solve engineering problems | | | X | | |
3 | 3. An ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability | | X | | | |
4 | 4. An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice | | X | | | |
5 | 5. An ability to design and conduct experiments, as well as to analyze and interpret data | X | | | | |
6 | 6. An ability to function on multidisciplinary teams | | | | | |
7 | 7. An ability to communicate effectively | | | | | |
8 | 8. A recognition of the need for, and an ability to engage in life-long learning | X | | | | |
9 | 9. An understanding of professional and ethical responsibility | | | | | |
10 | 10. A knowledge of contemporary issues | | | | | |
11 | 11. The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context | | | | | |
Assessment Methods
Contribution Level | Absolute Evaluation |
Rate of Midterm Exam to Success | | 30 |
Rate of Final Exam to Success | | 70 |
Total | | 100 |
ECTS / Workload Table |
Activities | Number of | Duration(Hour) | Total Workload(Hour) |
Course Hours | 14 | 3 | 42 |
Guided Problem Solving | 0 | 0 | 0 |
Resolution of Homework Problems and Submission as a Report | 0 | 0 | 0 |
Term Project | 0 | 0 | 0 |
Presentation of Project / Seminar | 0 | 0 | 0 |
Quiz | 0 | 0 | 0 |
Midterm Exam | 1 | 24 | 24 |
General Exam | 1 | 24 | 24 |
Performance Task, Maintenance Plan | 0 | 0 | 0 |
Total Workload(Hour) | 90 |
Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(90/30) | 3 |
ECTS of the course: 30 hours of work is counted as 1 ECTS credit. |
Detail Informations of the Course
Course Description
Course | Code | Semester | T+P (Hour) | Credit | ECTS |
---|
INTRODUCTION to MACHINE LEARNING | COE3167980 | Fall Semester | 3+0 | 3 | 6 |
Prerequisites Courses | |
Recommended Elective Courses | |
Language of Course | English |
Course Level | First Cycle (Bachelor's Degree) |
Course Type | Elective |
Course Coordinator | Prof.Dr. Bahadır Kürşat GÜNTÜRK |
Name of Lecturer(s) | Prof.Dr. Bahadır Kürşat GÜNTÜRK |
Assistant(s) | |
Aim | To be able to apply and evaluate machine learning techniques. |
Course Content | This course contains; Elements of machine learning,Regression,Basics of classification,Bayesian classifier,Logistic regression,Support vector machines,Neural networks,Convolutional neural networks,Decision trees,Ensemble methods,Feature selection,Principal component analysis,Clustering,Model evaluation. |
Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
Applies regression techniques | 12, 14, 16, 6, 9 | A, E |
Evaluates classification techniques | 12, 14, 16, 6, 9 | A, E |
Applies unsupervised machine learning techniques | 12, 14, 16, 6, 9 | A, E |
Applies feature selection / analysis techniques | 12, 14, 16, 6, 9 | A, E |
Teaching Methods: | 12: Problem Solving Method, 14: Self Study Method, 16: Question - Answer Technique, 6: Experiential Learning, 9: Lecture Method |
Assessment Methods: | A: Traditional Written Exam, E: Homework |
Course Outline
Order | Subjects | Preliminary Work |
---|
1 | Elements of machine learning | |
2 | Regression | |
3 | Basics of classification | |
4 | Bayesian classifier | |
5 | Logistic regression | |
6 | Support vector machines | |
7 | Neural networks | |
8 | Convolutional neural networks | |
9 | Decision trees | |
10 | Ensemble methods | |
11 | Feature selection | |
12 | Principal component analysis | |
13 | Clustering | |
14 | Model evaluation | |
Resources |
Bishop, “Pattern Recognition and Machine Learning,” Springer, (1st
edition)
Duda, Hart, and Stork, “Pattern Classification,” Wiley-Interscience, (2nd
edition) |
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications |
No | Program Qualification | Contribution Level |
1 | 2 | 3 | 4 | 5 |
1 | 1. An ability to apply knowledge of mathematics, science, and engineering | | | | X | |
2 | 2. An ability to identify, formulate, and solve engineering problems | | | X | | |
3 | 3. An ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability | | X | | | |
4 | 4. An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice | | X | | | |
5 | 5. An ability to design and conduct experiments, as well as to analyze and interpret data | X | | | | |
6 | 6. An ability to function on multidisciplinary teams | | | | | |
7 | 7. An ability to communicate effectively | | | | | |
8 | 8. A recognition of the need for, and an ability to engage in life-long learning | X | | | | |
9 | 9. An understanding of professional and ethical responsibility | | | | | |
10 | 10. A knowledge of contemporary issues | | | | | |
11 | 11. The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context | | | | | |
Assessment Methods
Contribution Level | Absolute Evaluation |
Rate of Midterm Exam to Success | | 30 |
Rate of Final Exam to Success | | 70 |
Total | | 100 |
Numerical Data
Ekleme Tarihi: 09/10/2023 - 10:50Son Güncelleme Tarihi: 09/10/2023 - 10:51
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