13E054NM - Neural Networks
Course specification | ||||
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Course title | Neural Networks | |||
Acronym | 13E054NM | |||
Study programme | Electrical Engineering and Computing | |||
Module | ||||
Type of study | bachelor academic studies | |||
Lecturer (for classes) | ||||
Lecturer/Associate (for practice) | ||||
Lecturer/Associate (for OTC) | ||||
ESPB | 6.0 | Status | elective | |
Condition | none | |||
The goal | Introduce students to the basic concepts of neural networks technology, different architectures, internal and external signal representation, learning ability and distributed information processing capability. Design neural network systems for typical engineering applications, including algorithms for signal processing, classification, regression, knowledge extraction. | |||
The outcome | Students will be able to independently analyze and synthesize different types of neural networks that are applied in many areas of engineering and learn to apply various algorithms for learning and training of neural networks and their implementation using MATLAB and Neural Network toolbox | |||
Contents | ||||
URL to the subject page | https://automatika.etf.bg.ac.rs/sr/13e054nm | |||
URL to lectures | https://teams.microsoft.com/l/team/19%3aA7XKjG47iuR_1XzyaGLls2ZRFB_X5LiWKnP391HqCK81%40thread.tacv2/conversations?groupId=3adb00c5-eb90-44f6-a500-18bd7fb3f8ca&tenantId=1774ef2e-9c62-478a-8d3a-fd2a495547ba | |||
Contents of lectures | Neural network history and types of problems: function approximation, classification, data clustering, time series, and dynamic systems modelling. Backpropagation algorithm, generalization, overfitting and initialization. Convergence properties of the BP algorithm. Modeling of time series and dynamic systems using nonlinear NN, classification and clustering, NN classifiers.CNN. Deep learning. | |||
Contents of exercises | Computer exercises with demonstrations and training algorithms for the design of neural networks. Solve practical problems in various fields of engineering with the help of neural networks using MATLAB Neural Networks toolbox. | |||
Literature | ||||
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Number of hours per week during the semester/trimester/year | ||||
Lectures | Exercises | OTC | Study and Research | Other classes |
3 | 1 | 1 | ||
Methods of teaching | Lectures, exercises on computers | |||
Knowledge score (maximum points 100) | ||||
Pre obligations | Points | Final exam | Points | |
Activites during lectures | 0 | Test paper | 30 | |
Practical lessons | 0 | Oral examination | 0 | |
Projects | ||||
Colloquia | 40 | |||
Seminars | 30 |