University :Mansoura University |
Faculty :Faculty of Computers and Information |
Department :Information Systems |
|
| 1- Course data :- |
| | Code: | نمع 497 | | Course title: | Selected Topics-1 - الشبكات العصبيه | | Year/Level: | رابعة نظم المعلومات | | Program Title: | | | Specialization: | | | Teaching Hours: | Theoretical: | 3 | Tutorial: | | Practical: | 3 |
|
| 2- Course aims :- |
| - This aim of this course is to give the participants a good background about artificial neural networks. It introduces the concepts of connectionism, along with algorithms for simulating neural networks, discussion of alternative neural 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
|
| 3- Course Learning Outcomes :- |
| |
| 4- Course contents :- |
| | No | Topics | Week |
|---|
| 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 | Neural Networks Based on Competition | | | 9 | Neural Networks Based on Competition | | | 10 | Hardware implementation of neural networks | | | 11 | Neural network applications | | | 12 | Review and discussion | |
|
|
| 5- Teaching and learning methods :- |
| | S | Method |
|---|
| Computer | | Data Show | | power point slides | | whiteboard | | different software |
|
|
| 6- Teaching and learning methods of disables :- |
| No data found. |
|
| 7- Student assessment :- |
| A. Timing |
| | No | Method | Week |
|---|
| 1 | mid-term exam | 10 | | 2 | reports | 14 | | 3 | oral | 14 |
|
| B. Degree |
| | No | Method | Degree |
|---|
| 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 | | | Total | 100% |
|
|
| 8- List of books and references |
| | S | Item | 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. | | | 3 | Laurene V. V. Fausett "Fundamentals of neural networks, architectures, algorithms, and applications," Prentice Hall, Inc. 1993. | |
|
|
| 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) | | | Neural Networks Based on Competition | | | Neural Networks Based on Competition | | | Hardware implementation of neural networks | | | Neural network applications | | | Review and discussion | |
|
|
| Course Coordinator(s): - |
| - Hazem Mokhtar Mokhtar El Bakry
|
| Head of department: - |
| Alaa El din Mohamed Riad |
