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The key task of the Machine Learning Department is to carry out research into the theoretical rudiments of machine learning algorithms and related biologically inspired optimization algorithms, and to promote their use within the gamut of applications based primarily on data separation and on the prediction of time series in the field of science and engineering, and in other social spheres.
As a subdivision of the computer sciences, machine learning deals with research into systems that learn solely on the basis of the knowledge of data. These systems currently form the backbone of artificial intelligence applications in diverse branches of science, industry, health care and sociology. An upsurge of artificial intelligence applications based on machine learning methods, a process evident in the past few years(1), has been facilitated by two aspects in particular. Firstly, by the wide-ranging availability of data describing the processes under scrutiny, which is also associated with the spread of digital scanning of information for its subsequent processing, and secondly by the significant progress in the performance of computer technology and its massive parallelization. These two factors have made it possible to use methods of artificial intelligence in a broad range of human activities, while the current growth of computing performance has caused that in some non-trivial applications artificial intelligence utilizing machine learning methods exceeds many times human capabilities (chess or the game go may be mentioned as graphic examples).
Many applications of machine learning are based on the use of computer structures described as deep neural networks, their variants and also on the optimization distributed methods of the genetic algorithm type. In spite of the indisputable practical success of this particular approach, the theoretical fundamentals of these methods have not yet been sufficiently explored. This tends to limit other possibilities for developing these applications to what is today the predominant empirical approach, which, however, lacks efficiency given by that exact approach based on profound knowledge of the principles on which the above-mentioned methods are known to be operating.
Researchers of the Machine Learning Department are focused on studying theoretical properties of machine learning methods, especially on exploring the properties of deep neural networks optimized by supervised learning, on examining different variants and designs of such models, and on clarifying related phenomena, among other things, convergence speed of learning, statistical reliability of learnt networks, robustness towards outliers or adversarial examples and other issues.
A long-term task facing the Machine Learning Department is to achieve in-depth and broad understanding of the machine learning principles and to use such knowledge for designing more efficient methods in applications based on machine learning.
In view of the existing indications that higher levels of human intelligence are activated by similar neural structures as cognitive abilities, it is anticipated that understanding of the actual principles of machine learning should, at the same time, be conducive to creating new theoretical branches of informatics that will capture those higher levels of human intelligence.
On convergence of kernel density estimates in particle filtering, 2016, D. Coufal
On locally most powerful sequential rank tests, 2017, J. Kalina
Implicitly Weighted Methods in Robust Image Analysis, 2012, J. Kalina
Human-Inspired Eigenmovement Concept Provides Coupling-Free Sensorimotor Control in Humanoid Robot, 2017, A. V. Alexandrov, V. Lippi, T. Mergner, A. A. Frolov, G. Hettich and D. Húsek
Comparison of Seven Methods for Boolean Factor Analysis and Their Evaluation by Information Gain, 2015, A. A. Frolov, D. Húsek, P. Y. Polyakov
Probabilistic lower bounds for approximation by shallow perceptron networks, 2017, V. Kůrková, M. Sanguineti
Constructive lower bounds on model complexity of shallow perceptron networks, 2017, V. Kůrková
Measures of ruleset quality for general rules extraction methods, 2015, M. Holeňa
Using Copulas in Data Mining Based on the Observational Calculus, 2015, M. Holeňa, L. Bajer, M. Ščavnický
Evolution Strategies for Deep Neural Network Models Design, 2017, M. Vidnerová, R. Neruda
The department organizes a seminar HORA INFORMATICAE with both local and international speakers. Our colleague Martin Holeňa organizes Seminar of Machine learning and Modeling.
Estimates of the number of patterns in the PAC model for nonconsistent separation.
Analysis of data-mining methods based on univalent neural networks.
Robust classification for high-dimensional data.
Robust estimation in neural networks.
Head: František Hakl
Secretarial support: Iveta Kubíková