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Course Detail

Course Description

CourseCodeSemesterT+P (Hour)CreditECTS
ARTIFICIAL NEURAL NETWORKS-Fall Semester3+036
Course Program
Prerequisites Courses
Recommended Elective Courses
Language of CourseEnglish
Course LevelFirst Cycle (Bachelor's Degree)
Course TypeElective
Course CoordinatorAssist.Prof. Mehmet KOCATÜRK
Name of Lecturer(s)Assist.Prof. Mehmet KOCATÜRK
Assistant(s)
AimThe 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 ContentThis 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 KazanımlarıTeaching MethodsAssessment Methods
Designs single layer perceptron.10, 14, 16, 19, 2, 21, 3, 6, 8, 9A, E, F
Implements the online learning algorithm. 10, 14, 16, 19, 2, 21, 3, 6, 8, 9A, E, F
Develops classifiers using multilayer perceptrons. 10, 14, 16, 19, 2, 21, 3, 6, 8, 9A, E, F
Designs multilayer perceptron for regression. 10, 14, 16, 19, 2, 21, 3, 6, 8, 9A, 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

OrderSubjectsPreliminary Work
1The Nervous System: Microscopic View
2The Nervous System: Macroscopic View
3Machine Learning
4Perceptron
5Multilayer Perceptron
6Supervised Learning
7Backpropogation Algorithm
8Online Learning
9Batch Learning
10Overfitting
11Neural Networks for Pattern Classification
12Neural Networks in Regression
13Neuromodulation
14Reinforcement 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
NoProgram QualificationContribution Level
12345
1
An ability to apply knowledge of mathematics, science, and engineering
2
An ability to identify, formulate, and solve engineering problems
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
An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice
X
5
An ability to design and conduct experiments, as well as to analyze and interpret data
X
6
An ability to function on multidisciplinary teams
7
An ability to communicate effectively
8
A recognition of the need for, and an ability to engage in life-long learning
9
An understanding of professional and ethical responsibility
10
A knowledge of contemporary issues
11
The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context

Assessment Methods

Contribution LevelAbsolute Evaluation
Rate of Midterm Exam to Success 30
Rate of Final Exam to Success 70
Total 100
ECTS / Workload Table
ActivitiesNumber ofDuration(Hour)Total Workload(Hour)
Course Hours14342
Guided Problem Solving000
Resolution of Homework Problems and Submission as a Report51575
Term Project000
Presentation of Project / Seminar12020
Quiz000
Midterm Exam15050
General Exam000
Performance Task, Maintenance Plan000
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

CourseCodeSemesterT+P (Hour)CreditECTS
ARTIFICIAL NEURAL NETWORKS-Fall Semester3+036
Course Program
Prerequisites Courses
Recommended Elective Courses
Language of CourseEnglish
Course LevelFirst Cycle (Bachelor's Degree)
Course TypeElective
Course CoordinatorAssist.Prof. Mehmet KOCATÜRK
Name of Lecturer(s)Assist.Prof. Mehmet KOCATÜRK
Assistant(s)
AimThe 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 ContentThis 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 KazanımlarıTeaching MethodsAssessment Methods
Designs single layer perceptron.10, 14, 16, 19, 2, 21, 3, 6, 8, 9A, E, F
Implements the online learning algorithm. 10, 14, 16, 19, 2, 21, 3, 6, 8, 9A, E, F
Develops classifiers using multilayer perceptrons. 10, 14, 16, 19, 2, 21, 3, 6, 8, 9A, E, F
Designs multilayer perceptron for regression. 10, 14, 16, 19, 2, 21, 3, 6, 8, 9A, 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

OrderSubjectsPreliminary Work
1The Nervous System: Microscopic View
2The Nervous System: Macroscopic View
3Machine Learning
4Perceptron
5Multilayer Perceptron
6Supervised Learning
7Backpropogation Algorithm
8Online Learning
9Batch Learning
10Overfitting
11Neural Networks for Pattern Classification
12Neural Networks in Regression
13Neuromodulation
14Reinforcement 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
NoProgram QualificationContribution Level
12345
1
An ability to apply knowledge of mathematics, science, and engineering
2
An ability to identify, formulate, and solve engineering problems
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
An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice
X
5
An ability to design and conduct experiments, as well as to analyze and interpret data
X
6
An ability to function on multidisciplinary teams
7
An ability to communicate effectively
8
A recognition of the need for, and an ability to engage in life-long learning
9
An understanding of professional and ethical responsibility
10
A knowledge of contemporary issues
11
The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context

Assessment Methods

Contribution LevelAbsolute Evaluation
Rate of Midterm Exam to Success 30
Rate of Final Exam to Success 70
Total 100

Numerical Data

Student Success

Ekleme Tarihi: 09/10/2023 - 10:37Son Güncelleme Tarihi: 09/10/2023 - 10:37