Neural Networks

Parametric description

Lecturer    
Andrzej Kordecki, Ph.D.    
Course number and semester ECTS Classes per week

Neural Networks (ML.ANK385),

Neural networks for classification and identification (ML.EM05)

ML.ANK385: ECTS – 3
ML.EM05: ECTS – 5
ML.ANK385: W - 2
ML.EM05: W-2, C-1

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Rules

application/pdf Rules (ML.ANK385 and ML.EM05) (142,33 kB, 2/12/2019 10:02)

Prerequisites

  • Basics of mathematical analysis, algebra and statistics from the previous years of studies,
  • Basics of optimizing methods nonlinear functions,
  • Basic of numerical methods and programming languages in Matlab or Python.

Course Aims

  • Fundamentals of neural network operation and training. Knowledge and skills necessary for independent problem solving using neural networks.
  • Fundamentals of neural networks types and applications with an indication of advantages and limitations.

Lecture:

  • Introduction: basic ideas, history, applications. Description of the neuron, activation functions, basic characteristics of neural networks. Neural network structures: feedforward and recurrent.
  • Machine learning types: supervised, unsupervised and reinforced. Characteristics of input data and data augmentation in neural network learning. Application of neural networks in static and dynamic modeling.
  • Loss function and evaluation measures of network results used in regression and classification. Perceptron learning rule. On-line and off-line training. Gradient optimization methods in neural network learning. The Widrow-Hoff rule. Back propagation algorithm and its characteristics.
  • Characteristics of overfitting and underfitting in neural networks training. Validation methods. Generalization methods of neon networks: regularization, dropout, batch normlization.
  • Neural networks with radial base functions. Support vector machine. Self-organizing maps, Kohonen networks. LSTM recurrent network
  • Convolutional neural networks: description of ideas, structures, learning and their characteristics. Description of basic structures of neural networks in applications of computer vision

Exercise:

  • Basics of Python and Matlabie programming,
  • Tensorflow and Keras libraries in neural network problem formulation,
  • Practical applications of neural networks.

Recommended reading

  • S. Haykin, Neural Networks and Learning Machines, Third Edition, Prentice Hall, 2009
  • I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, MIT Press, 2016
  • G. Dreyfus, Neural Networks: Methodology and Applications, Springer, New York 2005
  • S. S. Haykin, Neural Networks and Learning Machines. Prentice Hall, 2009.

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