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Information
| Code | CEN402 |
| Name | Artificial Neural Networks |
| Term | 2024-2025 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 |
| Label | E Elective |
| 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 | Adequate knowledge of mathematics, science and related engineering disciplines; ability to use theoretical and applied knowledge in these fields in solving complex engineering problems. | 3 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Ability to identify, formulate and solve complex engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose. | 3 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions; ability to apply modern design methods for this purpose. | 4 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Ability to select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering practice; ability to use information technologies effectively. | 3 |
| PLO05 | Bilgi - Kuramsal, Olgusal | Ability to design and conduct experiments, collect data, analyze and interpret results to investigate complex engineering problems or discipline-specific research topics. | 4 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Ability to work effectively in interdisciplinary and multidisciplinary teams; individual working skills. | 2 |
| PLO07 | Bilgi - Kuramsal, Olgusal | Ability to communicate effectively verbally and in writing; knowledge of at least one foreign language; ability to write effective reports and understand written reports, prepare design and production reports, make effective presentations, and give and receive clear and understandable instructions. | 2 |
| PLO08 | Bilgi - Kuramsal, Olgusal | Awareness of the necessity of lifelong learning; ability to access information, follow developments in science and technology, and constantly renew oneself. | 3 |
| PLO09 | Bilgi - Kuramsal, Olgusal | Knowledge of ethical principles, professional and ethical responsibility, and standards used in engineering practice. | 4 |
| PLO10 | Bilgi - Kuramsal, Olgusal | Knowledge of business practices such as project management, risk management and change management; awareness of entrepreneurship and innovation; knowledge of sustainable development. | 4 |
| PLO11 | Bilgi - Kuramsal, Olgusal | Knowledge of the effects of engineering practices on health, environment and safety in universal and social dimensions and the problems of the age reflected in the field of engineering; awareness of the legal consequences of engineering solutions. | 3 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Slope reduction and uplift methods and applications for engineering problems | Read the related section of the book | |
| 2 | Biological and artificial nerve cells, neural cell models | Read the related section of the book | |
| 3 | Learning with a teacher algorithms : Perceptron Learning | Read the related section of the book | |
| 4 | Basic network topologies and Multi-layer Perceptron network (MLP) | Read the related section of the book Homework 1 | |
| 5 | Learning error back propagation | Read the related section of the book Homework 2 | |
| 6 | Radial basis function networks | Read the related section of the book | |
| 7 | General Regression Neural Network (GRNN) | Reading the lecture notes | |
| 8 | Mid-Term Exam | Read the related section of the book Homework 3 | |
| 9 | Probabilistic Neural Network (PNN) | Read the related section of the book Homework 4 | |
| 10 | Learning without a teacher and Hamming network | Read the related section of the book Homework 5 | |
| 11 | Mexican hat and MaxNet networks | Read the related section of the book Homework 6 | |
| 12 | Learning Vector Quantization (LVQ) | Read the related section of the book | |
| 13 | Self-Organizing Maps (SOM) | Read the related section of the book | |
| 14 | Adaptive Resonance Theory Neural Networks | Read the related section of the book | |
| 15 | Principal Component Analysis | Read the related section of the book | |
| 16 | Term Exams | Reviewing lecture notes | |
| 17 | Term Exams | Reviewing lecture notes |
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 | ||