University :Mansoura University |
Faculty :Faculty of Computers and Information |
Department :Computer Science |
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| 1- 1- Course data :- |
| | Code: | عح374 | | Course title: | Neural Networks | | Year/Level: | رابعة علوم الحاسب | | Program Title: | | | Specialization: | | | Teaching Hours: | Theoretical: | 3 | Tutorial: | 3 | Practical: | |
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| 2- 2- Course aims :- |
| - This course introduces the concepts of connectionism, along with algorithms for simulating neural networks, discussion of alternative network architectures and training algorithms
- understand how to design and implement artificial neural networks
- increase ability to solve existing problems by using artificial neural networks
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| 3- 3- Course Learning Outcomes :- |
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| 4- 4- Course contents :- |
| | No | Topics | Week No. |
|---|
| 1 | Introductio to neural networks | | | 2 | Differences between biological and artificial neuron | | | 3 | Design of artificial neuron | | | 4 | Types of activation functions | | | 5 | Neural network architecture | | | 6 | Learning different Models of neural networks | | | 7 | Training Algorithms (Hebbian, Perceptron, Adaline, Madaline, backpropagation) | | | 8 | Training neural networks for Realization of logic functions | | | 9 | Neural Networks Based on Competition | | | 10 | Hardware implementation of neural networks | | | 11 | Neural network applications | | | 12 | Review and discussion | |
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| 5- 5- Teaching and learning methods :- |
| | S | Method |
|---|
| Computer | | Data Show | | power point slides | | whiteboard | | different software |
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| 6- 6- Teaching and learning methods of disables :- |
| No data found. |
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| 7- 7- Student assessment :- |
| - Timing |
| | No | Method | Week No. |
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| 1 | mid-term exam | 10 | | 2 | reports | 12 | | 3 | oral | 12 |
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| - Degree |
| | No | Method | Degree |
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| 1 | Mid_term examination | 5 | | 2 | Final_term examination | 75 | | 3 | Oral examination | 5 | | 4 | Practical examination | 10 | | 5 | Semester work | 5 | | 6 | Other types of asessment | 0 | | Total | 100% |
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| 8- 8- List of books and references |
| | S | Reference | Type |
|---|
| 1 | Simon Hayken " Neural Networks: A Comprehensive Foundation," Prentice-Hall, Inc. 1999. | | | 2 | J. Hertz, A. Krogh, and R. G. Palmer. Introduction to the Theory of Neural Computation, Addison-Wesley, 1991. | |
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| 9- 9- Matrix of knowledge and skills of the course |
| | S | Content | Study week |
|---|
| Introductio to neural networks | | | Differences between biological and artificial neuron | | | Design of artificial neuron | | | Types of activation functions | | | Neural network architecture | | | Learning different Models of neural networks | | | Training Algorithms (Hebbian, Perceptron, Adaline, Madaline, backpropagation) | | | Training neural networks for Realization of logic functions | | | Neural Networks Based on Competition | | | Hardware implementation of neural networks | | | Neural network applications | | | Review and discussion | |
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| Course Coordinator(s): - |
| - Hazem Mokhtar Mokhtar El Bakry
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| Head of department: - |
| Alaa El din Mohamed Riad |
