Course Detail
Course Detail
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| ARTIFICIAL NEURAL NETWORKS | IND3168050 | 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 | Assist.Prof. Mehmet KOCATÜRK |
| Name of Lecturer(s) | Assist.Prof. Mehmet KOCATÜRK |
| Assistant(s) | |
| Aim | The aim of the course is to evaluate the use of the computational models of the neurons in machine learning and the modeling of the components of the nervous system. |
| Course Content | This course contains; The Nervous System: Microscopic View,The Nervous System: Macroscopic View,Machine Learning,Perceptron,Multilayer Perceptron,Supervised Learning,Backpropogation Algorithm,Online Learning,Batch Learning,Overfitting,Neural Networks for Pattern Classification,Neural Networks in Regression,Neuromodulation,Reinforcement Learning. |
| Course Learning Outcomes | Teaching Methods | Assessment Methods |
| Designs single layer perceptron. | 10, 14, 16, 19, 2, 21, 3, 6, 8, 9 | A, E, F |
| Implements the online learning algorithm. | 10, 14, 16, 19, 2, 21, 3, 6, 8, 9 | A, E, F |
| Develops classifiers using multilayer perceptrons. | 10, 14, 16, 19, 2, 21, 3, 6, 8, 9 | A, E, F |
| Designs multilayer perceptron for regression. | 10, 14, 16, 19, 2, 21, 3, 6, 8, 9 | A, E, F |
| Teaching Methods: | 10: Discussion Method, 14: Self Study Method, 16: Question - Answer Technique, 19: Brainstorming Technique, 2: Project Based Learning Model, 21: Simulation Technique, 3: Problem Baded Learning Model, 6: Experiential Learning, 8: Flipped Classroom Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, E: Homework, F: Project Task |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | The Nervous System: Microscopic View | |
| 2 | The Nervous System: Macroscopic View | |
| 3 | Machine Learning | |
| 4 | Perceptron | |
| 5 | Multilayer Perceptron | |
| 6 | Supervised Learning | |
| 7 | Backpropogation Algorithm | |
| 8 | Online Learning | |
| 9 | Batch Learning | |
| 10 | Overfitting | |
| 11 | Neural Networks for Pattern Classification | |
| 12 | Neural Networks in Regression | |
| 13 | Neuromodulation | |
| 14 | Reinforcement Learning |
| Resources |
| Alpaydin, E., (2010) Introduction to machine learning, MIT Press,Cambridge. Kandel, E. R., Schwartz, J. H., Jessell, T. M., Siegelbaum, S. A., Hudspeth, A. J. , (2012) Principles of neural science, McGraw-Hill, New York. |
| Lytton, W. W., (2002) From computer to brain : foundations of computational neuroscience, Springer, New York. Dayan, P., Abbott, L. F., (2001) Theoretical neuroscience: Computational and mathematical modeling of neural systems, MIT Press, Cambridge. Izhikevich, E.M., (2007) Dynamical systems in neuroscience: The geometry of excitability and bursting, MIT Press, Cambridge. |
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. | X | |||||
| 2 | Ability to formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose. | X | |||||
| 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. | X | |||||
| 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. | X | |||||
| 5 | Ability to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or discipline specific research questions. | X | |||||
| 6 | Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually. | X | |||||
| 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. | X | |||||
| 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. | X | |||||
| 9 | Knowledge on behavior according ethical principles, professional and ethical responsibility and standards used in engineering practices. | X | |||||
| 10 | Knowledge about business life practices such as project management, risk management, and change management; awareness in entrepreneurship, innovation; knowledge about sustainable development. | X | |||||
| 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. | X | |||||
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 | 5 | 15 | 75 | |||
| Term Project | 0 | 0 | 0 | |||
| Presentation of Project / Seminar | 1 | 20 | 20 | |||
| Quiz | 0 | 0 | 0 | |||
| Midterm Exam | 1 | 50 | 50 | |||
| General Exam | 0 | 0 | 0 | |||
| Performance Task, Maintenance Plan | 0 | 0 | 0 | |||
| Total Workload(Hour) | 187 | |||||
| Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(187/30) | 6 | |||||
| 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 |
|---|---|---|---|---|---|
| ARTIFICIAL NEURAL NETWORKS | IND3168050 | 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 | Assist.Prof. Mehmet KOCATÜRK |
| Name of Lecturer(s) | Assist.Prof. Mehmet KOCATÜRK |
| Assistant(s) | |
| Aim | The aim of the course is to evaluate the use of the computational models of the neurons in machine learning and the modeling of the components of the nervous system. |
| Course Content | This course contains; The Nervous System: Microscopic View,The Nervous System: Macroscopic View,Machine Learning,Perceptron,Multilayer Perceptron,Supervised Learning,Backpropogation Algorithm,Online Learning,Batch Learning,Overfitting,Neural Networks for Pattern Classification,Neural Networks in Regression,Neuromodulation,Reinforcement Learning. |
| Course Learning Outcomes | Teaching Methods | Assessment Methods |
| Designs single layer perceptron. | 10, 14, 16, 19, 2, 21, 3, 6, 8, 9 | A, E, F |
| Implements the online learning algorithm. | 10, 14, 16, 19, 2, 21, 3, 6, 8, 9 | A, E, F |
| Develops classifiers using multilayer perceptrons. | 10, 14, 16, 19, 2, 21, 3, 6, 8, 9 | A, E, F |
| Designs multilayer perceptron for regression. | 10, 14, 16, 19, 2, 21, 3, 6, 8, 9 | A, E, F |
| Teaching Methods: | 10: Discussion Method, 14: Self Study Method, 16: Question - Answer Technique, 19: Brainstorming Technique, 2: Project Based Learning Model, 21: Simulation Technique, 3: Problem Baded Learning Model, 6: Experiential Learning, 8: Flipped Classroom Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, E: Homework, F: Project Task |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | The Nervous System: Microscopic View | |
| 2 | The Nervous System: Macroscopic View | |
| 3 | Machine Learning | |
| 4 | Perceptron | |
| 5 | Multilayer Perceptron | |
| 6 | Supervised Learning | |
| 7 | Backpropogation Algorithm | |
| 8 | Online Learning | |
| 9 | Batch Learning | |
| 10 | Overfitting | |
| 11 | Neural Networks for Pattern Classification | |
| 12 | Neural Networks in Regression | |
| 13 | Neuromodulation | |
| 14 | Reinforcement Learning |
| Resources |
| Alpaydin, E., (2010) Introduction to machine learning, MIT Press,Cambridge. Kandel, E. R., Schwartz, J. H., Jessell, T. M., Siegelbaum, S. A., Hudspeth, A. J. , (2012) Principles of neural science, McGraw-Hill, New York. |
| Lytton, W. W., (2002) From computer to brain : foundations of computational neuroscience, Springer, New York. Dayan, P., Abbott, L. F., (2001) Theoretical neuroscience: Computational and mathematical modeling of neural systems, MIT Press, Cambridge. Izhikevich, E.M., (2007) Dynamical systems in neuroscience: The geometry of excitability and bursting, MIT Press, Cambridge. |
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. | X | |||||
| 2 | Ability to formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose. | X | |||||
| 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. | X | |||||
| 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. | X | |||||
| 5 | Ability to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or discipline specific research questions. | X | |||||
| 6 | Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually. | X | |||||
| 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. | X | |||||
| 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. | X | |||||
| 9 | Knowledge on behavior according ethical principles, professional and ethical responsibility and standards used in engineering practices. | X | |||||
| 10 | Knowledge about business life practices such as project management, risk management, and change management; awareness in entrepreneurship, innovation; knowledge about sustainable development. | X | |||||
| 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. | X | |||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 30 | |
| Rate of Final Exam to Success | 70 | |
| Total | 100 | |