Course Detail
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| INTRODUCTION to MACHINE LEARNING | - | Fall Semester | 3+0 | 3 | 6 |
| Course Program |
| 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 | Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied knowledge in these areas in the solution of complex engineering problems. | ||||||
| 2 | Ability to formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose. | ||||||
| 3 | Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose. | ||||||
| 4 | Ability to select and use modern techniques and tools needed for analyzing and solving complex problems encountered in engineering practice; ability to employ information technologies effectively. | ||||||
| 5 | Ability to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or discipline specific research questions. | ||||||
| 6 | Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually. | ||||||
| 7 | Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language; ability to write effective reports and comprehend written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions. | ||||||
| 8 | Awareness of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself. | ||||||
| 9 | Knowledge on behavior according ethical principles, professional and ethical responsibility and standards used in engineering practices. | ||||||
| 10 | Knowledge about business life practices such as project management, risk management, and change management; awareness in entrepreneurship, innovation; knowledge about sustainable development. | ||||||
| 11 | Knowledge about the global and social effects of engineering practices on health, environment, and safety, and contemporary issues of the century reflected into the field of engineering; awareness of the legal consequences of engineering solutions. | ||||||
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 | - | Fall Semester | 3+0 | 3 | 6 |
| Course Program |
| 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 | Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied knowledge in these areas in the solution of complex engineering problems. | ||||||
| 2 | Ability to formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose. | ||||||
| 3 | Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose. | ||||||
| 4 | Ability to select and use modern techniques and tools needed for analyzing and solving complex problems encountered in engineering practice; ability to employ information technologies effectively. | ||||||
| 5 | Ability to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or discipline specific research questions. | ||||||
| 6 | Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually. | ||||||
| 7 | Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language; ability to write effective reports and comprehend written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions. | ||||||
| 8 | Awareness of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself. | ||||||
| 9 | Knowledge on behavior according ethical principles, professional and ethical responsibility and standards used in engineering practices. | ||||||
| 10 | Knowledge about business life practices such as project management, risk management, and change management; awareness in entrepreneurship, innovation; knowledge about sustainable development. | ||||||
| 11 | Knowledge about the global and social effects of engineering practices on health, environment, and safety, and contemporary issues of the century reflected into the field of engineering; awareness of the legal consequences of engineering solutions. | ||||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 30 | |
| Rate of Final Exam to Success | 70 | |
| Total | 100 | |