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Course: Machine Learning

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Course title Machine Learning
Course code FES/ASU
Organizational form of instruction Lecture
Level of course Doctoral
Year of study not specified
Semester Winter and summer
Number of ECTS credits 10
Language of instruction Czech
Status of course unspecified
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Hájek Petr, prof. Ing. Ph.D.
Course content
Learning theory and classification of machine learning tasks Generative and discriminative learning algorithms Support vector machines and support vector regression Theoretical questions of training data selection. Selection and extraction of attributes. Ensembles of algorithms for supervised learning Semi-supervised learning Methods for learning algorithms evaluation Machine learning in information retrieval and knowledge extraction from text Machine learning applications in big data processing

Learning activities and teaching methods
unspecified
Learning outcomes
The aim of the course it to meet students with the newest approaches in the area of machine learning. Present trends in algorithm development of machine learning for different task types are discussed as well as selected application of machine learning are introduces, particularly in the field of huge data volume processing.
Students will gain knowledge on current advanced approaches to machine learning. They will also be able to design and implement machine learning systems for specific tasks.
Prerequisites
unspecified

Assessment methods and criteria
unspecified
The subject will be finished by the completion and successful defence of a project worked out by every participant of the course. The topic of this project should be related to student's doctoral dissertation thesis.
Recommended literature
  • BISHOP, C.M. Pattern Recognition and Machine Learning. New York, 2007.
  • DUDA, R., HART, P., STORK, D. Pattern Classification. New York, 2001.
  • GUYON, I., ELISSEEFF, A. An introduction to variable and feature selection. 2003.
  • HASTIE, T., TIBSHIRANI, R., FRIEDMAN, J. The Elements of Statistical Learning. Berlin, 2009.
  • CHAPELLE, O., SCHOLKOPF, B. Semi-Supervised Learning. Cambridge, 2006.
  • KONONENKO, I., KUKAR, M. Machine Learning and Data Mining. Amsterdam, 2007.
  • MANNING, C. D., RAGHAVAN, P., SCHÜTZE, H. Introduction to Information Retrieval. Cambridge, 2008.
  • STEINWART, I., CHRISTMANN, A. Support Vector Machines. Berlin, 2008.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester
University of Pardubice, date of update: 03.06.2024 23:50.