19D051NM - Neural Networks

Course specification
Course title Neural Networks
Acronym 19D051NM
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 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. CNN. Deep learning.
      Contents of exercises
      1. M. Nielson, Neural Networks and Deep Learning, Determination press, 2015
      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)
      5. C. Bishop, Neural networks and pattern recognition, Prentice Hall, 2000 (Original title)
      Number of hours per week during the semester/trimester/year
      Lectures Exercises OTC Study and Research Other classes
      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 0
      Colloquia 30
      Seminars 0