Course title | Machine Learning |
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Course code | FES/DSU |
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 | Compulsory-optional |
Form of instruction | Face-to-face |
Work placements | This is not an internship |
Recommended optional programme components | None |
Lecturer(s) |
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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
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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
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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 |
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Study plans that include the course |
Faculty | Study plan (Version) | Category of Branch/Specialization | Recommended semester | |
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Faculty: Faculty of Economics and Administration | Study plan (Version): Applied Informatics (2014) | Category: Informatics courses | - | Recommended year of study:-, Recommended semester: - |
Faculty: Faculty of Economics and Administration | Study plan (Version): Applied Informatics (2014) | Category: Informatics courses | - | Recommended year of study:-, Recommended semester: - |