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 title:||Machine learning and Neural networks|
|Course unit code:||I-SUNS|
|Mode of completion and Number of ECTS credits:||Exam (6 credits)|
|Name of lecturer:||Ing. Zuzana Bukovčiková (examiner, instructor, tutor) - slovak, english |
Ing. Pavol Marák (examiner, instructor, tutor) - slovak, english
prof. Dr. Ing. Miloš Oravec (examiner, instructor, lecturer, person responsible for course, tutor) - slovak, english
Ing. Dominik Sopiak, PhD. (examiner, instructor, tutor) - slovak, english
|Learning outcomes of the course unit:|
|The subject Machine Learning and Neural Networks aims to apprise the students of the theory and applications of machine learning. The subject offers systematic approach to the best known methods of machine learning with the emphasis on neural networks, boosting, kernel methods, support vector machines, clustering, deep learning. Following the study of theoretical foundations, a student is able to utilize these modern tools in various areas of information and communication technologies. The subject contains also machine learning applications (in the area of pattern recognition, biometrics, communication networks, signal processing etc.).|
|Prerequisites and co-requisites:||none|
|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.
|Recommended or required reading:|
|Planned learning activities and teaching methods:||lecture, exercise|
|Assesment methods and criteria:||-- item not defined --|
|Language of instruction:||Slovak, English|
|Work placement(s):||There is no compulsory work placement in the course unit.|
Last modification made by RNDr. Marian Puškár on 05/09/2019.