Course detail
Information Representation and Machine Learning
FEKT-DPA-IMLAcad. year: 2022/2023
Complexity theory, graph theory, graph equivalence, queuing theory, Petri nets, simulation and modeling, Markov models, advanced evolutionary algorithms.
Guarantor
Department
Offered to foreign students
The home faculty only
Learning outcomes of the course unit
Not applicable.
Prerequisites
Not applicable.
Co-requisites
Not applicable.
Recommended optional programme components
Not applicable.
Recommended or required reading
Goldreich, Oded. "Computational complexity: a conceptual perspective." ACM SIGACT News 39.3 (2008): 35-39. (EN)
Mitleton-Kelly, Eve. Complex systems and evolutionary perspectives on organisations: the application of complexity theory to organisations. Elsevier Science Ltd, 2003. (EN)
Bürgisser, Peter, Michael Clausen, and Amin Shokrollahi. Algebraic complexity theory. Vol. 315. Springer Science & Business Media, 2013. (EN)
Planned learning activities and teaching methods
Not applicable.
Assesment methods and criteria linked to learning outcomes
final examination
Language of instruction
English
Work placements
Not applicable.
Course curriculum
1. Information representation, introduction
2. Complexity theory, selected examples of complexity
3. Graph theory, analysis, factorization
4. Theory of graphs, groups, availability, bipartite
5. Graphs equivalence
6. Information representation - machine learning
7. Information representation - network types
8. Information representation - linear regression
9. Information representation - logistic regression, classification
10. Information representation - optimization
11. Reprezentace informace - dopředná neuronová síť
12, Evolutionary Algorithms
13. Multithreaded computing, parallelization
Aims
Objective of this course is to provide information about complexity theeory, graph theory and their comparison, queuing theory, Petri nets, evolution algorithms.