13S053NM - Neural Networks

Course specification
Course title Neural Networks
Acronym 13S053NM
Study programme Software Engineering
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 Concepts of neural networks technology, architectures, learning ability and distributed information processing capability. Introduction to convolutional neural networks, autoencoder networks, deep learning. Design neural network systems for typical engineering applications, pattern recognition, algorithms for signal processing, classification....
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/Python
Contents of lectures Overview of classic structures of neural networks, training, generalization and initialization of neural networks. Classification and clustering with neural networks. Convolutional neural networks. Autoencoder neural networks. Concepts of regularization, data augmentation, hyperparameters, activation functions, dropout, crossentropy... Architectures LeNet, AlexNet, VGG, Resnet...
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/Python
  1. Goodfellow, Ian. "Deep Learning-Ian Goodfellow, Yoshua Bengio, Aaron Courville- Google Books." (2016). (Original title)
  2. Michael A. Nielsen, "Neural Networks and Deep Learning", Determination Press, 2015 (Original title)
  3. Patterson, Josh, and Adam Gibson. Deep learning: A practitioner's approach. " O'Reilly Media, Inc.", 2017. (Original title)
  4. Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems, Chin-Teng Lin, C. S. George Lee, Prentice Hall, 1996
  5. Neural Networks for Pattern Recognition, Christopher Bishop, Oxford University Press, 2000
Number of hours per week during the semester/trimester/year
Lectures Exercises OTC Study and Research Other classes
2 2 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 12 Oral examination 0
Projects 18
Colloquia 40
Seminars 0