13D051NM - Neural Networks
Course specification | ||||
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Course title | Neural Networks | |||
Acronym | 13D051NM | |||
Study programme | Electrical Engineering and Computing | |||
Module | System Control and Signal Processing | |||
Type of study | doctoral studies | |||
Lecturer (for classes) | ||||
Lecturer/Associate (for practice) | ||||
Lecturer/Associate (for OTC) | ||||
ESPB | 9.0 | Status | elective | |
Condition | none | |||
The goal | Introduction to basic concepts of neural networks, different architectures, learning abilities of neural networks and so on. Training students to independently design neural networks for engineering applications, digital signal processing, telecommunications... | |||
The outcome | Students will be able to independently analyze and synthesize different types of neural networks that are applied in many areas of engineering. You will learn to apply various algorithms for learning and training of neural networks and their implementation using MATLAB. | |||
Contents | ||||
Contents of lectures | Overview of the history and architecture of neural networks, training, generalization and initialization of neural networks. Convergence properties of algorithms. Nonlinear dynamic black box. Classification and clustering with neural networks. Kohonen and Hopfield neural networks. | |||
Contents of exercises | ||||
Literature | ||||
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Number of hours per week during the semester/trimester/year | ||||
Lectures | Exercises | OTC | Study and Research | Other classes |
6 | ||||
Methods of teaching | lectures and auditory exercises | |||
Knowledge score (maximum points 100) | ||||
Pre obligations | Points | Final exam | Points | |
Activites during lectures | 0 | Test paper | 70 | |
Practical lessons | 0 | Oral examination | 0 | |
Projects | ||||
Colloquia | 30 | |||
Seminars | 0 |