| Course title | Data Analysis and Processing |
|---|---|
| Course code | KBC/SZZM4 |
| Organizational form of instruction | no contact |
| Level of course | Master |
| Year of study | not specified |
| Semester | Winter and summer |
| Number of ECTS credits | 0 |
| Language of instruction | Czech |
| Status of course | Compulsory |
| 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 |
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Data (data types, basic concepts). Clustering. Classification. Dependencies in data. Association rules. Analysis of graphs and structured data. Probabilistic methods of data representation. Graph models and Bayesian networks: decomposition, evidence propagation, learning graphical models, conjecture revision. Dimensionality reduction: basic methods (PCA, SVD). Advanced methods (NMF, BFA), deep learning: neural network based models and other models. Subject of artificial intelligence, development of artificial intelligence, social and philosophical issues of artificial intelligence. Problem solving. Expert systems, motivation and development of expert systems. Rule-based expert systems. Prolog language, expert systems in Prolog. Fuzzy logic; fuzzy logic rule systems. Artificial neural networks and deep learning, motivation, biological neural networks, simple perceptron, multilayer networks, RBF networks, associative neural networks, self-organizing maps. Evolutionary computation, swarm intelligence, knowledge representation, natural language processing. Entropy, conditional and joint entropy, mutual information. Basic inequalities of information theory. AEP and its applications. Selected applications of information theory. Basic concepts of coding. Optimal codes. Self-correcting codes (basic concepts, block codes, linear codes).
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| Learning activities and teaching methods |
Dialogic Lecture (Discussion, Dialog, Brainstorming)
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| Learning outcomes |
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The subject represents a final oral exam.
Successful completion will verify and confirm knowledge of the discipline acquired during studies |
| Prerequisites |
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Successful completion of the requirements of the follow-up Master's degree
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| Assessment methods and criteria |
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Oral exam
The requirement is to demonstrate on the basis of theoretical and practical studies candidate's skills and the ability to focus on the issue of the examined discipline. |
| Recommended literature |
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| Study plans that include the course |
| Faculty | Study plan (Version) | Category of Branch/Specialization | Recommended semester | |
|---|---|---|---|---|
| Faculty: Faculty of Science | Study plan (Version): Bioinformatics (2021) | Category: Informatics courses | 2 | Recommended year of study:2, Recommended semester: Summer |