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 delivery, planned learning activities and teaching methods:|
|Recommended semester/trimester:||Applied Informatics - master (compulsory), 1. semester|
|Level of study:||2.|
|Prerequisites for registration:||none|
|Active participation in the exercises and successful fulfillment of tasks. Successful completion of the exam. |
Total mark in the examination will be derived from a point expressed as follows:
-exercises max. 45 points
-written exam max. 55 points
Exam is successful provided the following criteria are met:
-20 points for exercise
-20 points for the written exam
Evaluation of the subject is according to the grading scale according to STU Study Regulations (Article 16, paragraph 3):
Success criteria (percentage of results in the evaluation of the subject) for classification grades are as follows:
a) A – 92 to 100%
b) B - 83 to 91%
c) C - 74 to 82%
d) D – 65 to 73%
e) E – 56 to 64%
f) FX – 0 to 55%
|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.).|
|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:|
|Language of instruction:||slovak or english|
|Assessed students in total: 477|
|Name of lecturer(s):||Ing. Zuzana Bukovčiková (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
|Last modification:||9. 5. 2019|
|Supervisor:||prof. Dr. Ing. Miloš Oravec and programme supervisor|
Last modification made by RNDr. Marian Puškár on 05/09/2019.