SylabUZ
Course name | Neural and neuro-fuzzy networks |
Course ID | 11.3-WE-INFD-NaN-fN-Er |
Faculty | Faculty of Engineering and Technical Sciences |
Field of study | WIEiA - oferta ERASMUS / Informatics |
Education profile | - |
Level of studies | Second-cycle Erasmus programme |
Beginning semester | winter term 2018/2019 |
Semester | 2 |
ECTS credits to win | 6 |
Course type | obligatory |
Teaching language | english |
Author of syllabus |
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The class form | Hours per semester (full-time) | Hours per week (full-time) | Hours per semester (part-time) | Hours per week (part-time) | Form of assignment |
Lecture | 30 | 2 | - | - | Exam |
Laboratory | 30 | 2 | - | - | Credit with grade |
Feedforward neural networks. Fundamentals of multilayer neural networks. Backpropagation algorithm for neural network learning. Issues and limitations of gradient descent learning algorithms. Adaptive learning rate. Momentum. Review of advanced learning algorithms. Sample applications of neural networks.
Recurrent neural networks. Dynamic-feedback neural networks. Locally recurrent globally feedforward networks. Learning algorithms for feedback neural networks. Hopfield neural network. Learning algorithms for Hopfield neural network.
Self-organizing neural networks. Kohonen self-organizing feature maps. Competitive learning. Algorithm of neural gas. Sample applications of the Kohonen network.
Deep learning. Convolutional neural network. Restricted Boltzman Machine. Deep Belief Networks. Autoencoders. Fast deep learning with GPU computations.
Neuro-fuzzy systems. Fuzzy sets and fuzzy logic. Operations on fuzzy sets. Fuzzy inference. Fuzzy rules. Mamdani and Takagi-Sugeno neuro-fuzzy systems. Gradient descent based learning algorithm for neuro-fuzzy systems.
Lecture, teaching laboratory classes.
Outcome description | Outcome symbols | Methods of verification | The class form |
Lecture - the passing criterion is a sufficient mark from the final test.
Laboratory - the passing criterion are positive marks for laboratory exercises and tests.
Final mark components = lecture: 50% + teaching laboratory: 50%
Modified by dr hab. inż. Marek Kowal, prof. UZ (last modification: 27-03-2018 18:09)