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.
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
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 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
EEE3168050
Fall Semester
3+0
3
6
Course Program
Cuma 09:00-09:45
Cuma 10:00-10:45
Cuma 11:00-11:45
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.
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
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
