Information sheet ECTS Syllabus
Course syllabus I-SUNS - Machine learning and Neural networks (FEEIT - WS 2019/2020)
|University:||Slovak University of Technology in Bratislava|
|Faculty:||Faculty of Electrical Engineering and Information Technology|
|Course unit code:||I-SUNS|
|Course unit title:||Machine learning and Neural networks|
|Mode of completion and Number of ECTS credits:||Exam (6 credits)|
|Concepts and principles (artificial intelligence, machine learning, computational intelligence, intelligent systems, knowledge discovery, neural network).
Supervised learning, unsupervised learning, learning theory, reinforcement learning.
Neurocomputing, differences to the classical approach, range of applications, analogies and differences in biological and artificial neural networks (ANN).
Models of a neuron, activation function, ANN architectures, learning process, ANN and mapping.
Multilayer perceptron, backpropagation algorithm, generalization, approximation of functions.
RBF (Radial-Basis Function) network, regularization theory.
Self-organizing systems based on competitive learning and the Hebbian learning.
Ensembles - boosting, strong and weak learning, AdaBoost.
Margins, kernel methods, support vector machines.
Deep learning, deep neural networks.
Applications of machine learning in pattern recognition and biometrics, in communications networks, in signal processing.
Last modification made by RNDr. Marian Puškár on 05/09/2019.