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Information
| Code | CEN402 |
| Name | Artificial Neural Networks |
| Term | 2022-2023 Academic Year |
| Semester | 8. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | İngilizce |
| Level | Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | |
| Course Instructor |
1 |
Course Goal / Objective
To gain the ability to use the artificial neural networks based on mathematical models of biological neural cell for modelling and solving engineering problems.
Course Content
History of Neural Networks, Fundamental Neural Networks, Statistical Pattern Recognition, Classification, Single-Layer Networks, Multi-Layer Networks-Backpropagation Model, Radial Basis Function, Error Functions.
Course Precondition
There are no prerequisites.
Resources
Neural Networks, S. Haykin, Prenctice Hall, Second Edition, 1999.
Notes
Neural Networks, S. Haykin, Prenctice Hall, Second Edition, 1999.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Use of mathematical base model for artificial neural network |
| LO02 | Understand the necessary mathematical base for neural networks |
| LO03 | Implementing multilayer perceptron neural network on software and apply it to real life problems |
| LO04 | Developing radial basis function |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Has capability in the fields of mathematics, science and computer that form the foundations of engineering | 3 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Identifies, formulates, and solves engineering problems, selects and applies appropriate analytical methods and modeling techniques, | 3 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Analyzes a system, its component, or process and designs under realistic constraints to meet the desired requirements,gains the ability to apply the methods of modern design accordingly. | 4 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Ability to use modern techniques and tools necessary for engineering practice and information technologies effectively. | 3 |
| PLO05 | Bilgi - Kuramsal, Olgusal | Ability to design and to conduct experiments, to collect data, to analyze and to interpret results | 4 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Has ability to work effectively as an individual and in multi-disciplinary teams, take sresponsibility and builds self-confidence | 2 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Can access information,gains the ability to do resource research and uses information resources | 2 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Awareness of the requirement of lifelong learning, to follow developments in science and technology and continuous self-renewal ability | 3 |
| PLO09 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Ability to communicate effectively orally and in writing, and to read and understand technical publications in at least one foreign language | 4 |
| PLO10 | Yetkinlikler - Öğrenme Yetkinliği | Professional and ethical responsibility, | 4 |
| PLO11 | Yetkinlikler - Öğrenme Yetkinliği | Awareness about project management, workplace practices, employee health, environmental and occupational safety, and the legal implications of engineering applications, | 3 |
| PLO12 | Yetkinlikler - Öğrenme Yetkinliği | Becomes aware of universal and social effects of engineering solutions and applications, entrepreneurship and innovation, and knowledge of contemporary issues |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Slope reduction and uplift methods and applications for engineering problems | Read the related section of the book | Öğretim Yöntemleri: Anlatım, Gösteri |
| 2 | Biological and artificial nerve cells, neural cell models | Read the related section of the book | Öğretim Yöntemleri: Anlatım, Gösteri |
| 3 | Learning with a teacher algorithms : Perceptron Learning | Read the related section of the book | Öğretim Yöntemleri: Anlatım |
| 4 | Basic network topologies and Multi-layer Perceptron network (MLP) | Read the related section of the book Homework 1 | Öğretim Yöntemleri: Anlatım |
| 5 | Learning error back propagation | Read the related section of the book Homework 2 | Öğretim Yöntemleri: Anlatım |
| 6 | Radial basis function networks | Read the related section of the book | Öğretim Yöntemleri: Anlatım |
| 7 | General Regression Neural Network (GRNN) | Reading the lecture notes | Öğretim Yöntemleri: Anlatım |
| 8 | Mid-Term Exam | Read the related section of the book Homework 3 | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Probabilistic Neural Network (PNN) | Read the related section of the book Homework 4 | Öğretim Yöntemleri: Anlatım |
| 10 | Learning without a teacher and Hamming network | Read the related section of the book Homework 5 | Öğretim Yöntemleri: Anlatım |
| 11 | Mexican hat and MaxNet networks | Read the related section of the book Homework 6 | Öğretim Yöntemleri: Anlatım |
| 12 | Learning Vector Quantization (LVQ) | Read the related section of the book | Öğretim Yöntemleri: Anlatım |
| 13 | Self-Organizing Maps (SOM) | Read the related section of the book | Öğretim Yöntemleri: Anlatım |
| 14 | Adaptive Resonance Theory Neural Networks | Read the related section of the book | Öğretim Yöntemleri: Anlatım |
| 15 | Principal Component Analysis | Read the related section of the book | Öğretim Yöntemleri: Anlatım |
| 16 | Term Exams | Reviewing lecture notes | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Reviewing lecture notes | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 30 | 30 |
| Total Workload (Hour) | 157 | ||
| Total Workload / 25 (h) | 6,28 | ||
| ECTS | 6 ECTS | ||