Course Detail
Course Description
Course | Code | Semester | T+P (Hour) | Credit | ECTS |
---|
ARTIFICIAL NEURAL NETWORKS | COE3168050 | 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 | 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. |
Dersin Öğrenme Kazanımları | 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 | 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 | | | | X | |
7 | 7. An ability to communicate effectively | | | X | | |
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 | X | | | | |
10 | 10. A knowledge of contemporary issues | X | | | | |
11 | 11. The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context | 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 | COE3168050 | 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 | 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. |
Dersin Öğrenme Kazanımları | 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 | 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 | | | | X | |
7 | 7. An ability to communicate effectively | | | X | | |
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 | X | | | | |
10 | 10. A knowledge of contemporary issues | X | | | | |
11 | 11. The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context | X | | | | |
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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