SI3NM - Neural Networks

Course specification
Course title Neural Networks
Acronym SI3NM
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 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 of lectures Neural network history and types of problems: function approximation, classification, data clustering, time series, and dynamic systems modelling. Learning and backpropagation algorithm, generalization, overfitting and initialization. Convergence properties of the BP algorithm. Modeling of time series and dynamic systems using nonlinear neural models, classification and clustering, nn classifiers.
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.
  1. Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems, Chin-Teng Lin, C. S. George Lee, Prentice Hall, 1996 (Original title)
  2. Neural Networks: A Comprehensive Foundation, 2nd edition. Simon Haykin, Prentice Hall, 1998 (Original title)
  3. Neural Networks for Pattern Recognition, Christopher Bishop, Oxford University Press, 2000 (Original title)
  4. Handbook of Neural Network Signal Processing, Ed. by Yu Hen Hu and Jenq-Neng Hwang, CRC Press, 2002 (Original title)
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 40
Practical lessons 0 Oral examination 0
Projects 20
Colloquia 40
Seminars 0