Course detail
Information Representation and Machine Learning
FEKT-DPA-IMLAcad. year: 2020/2021
Complexity theory, graph theory, graph equivalence, queuing theory, Petri nets, simulation and modeling, Markov models, advanced evolutionary algorithms.
Language of instruction
English
Number of ECTS credits
4
Mode of study
Not applicable.
Guarantor
Department
Learning outcomes of the course unit
Not applicable.
Prerequisites
Not applicable.
Co-requisites
Not applicable.
Planned learning activities and teaching methods
Not applicable.
Assesment methods and criteria linked to learning outcomes
final examination
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
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
Work placements
Not applicable.
Aims
Objective of this course is to provide information about complexity theeory, graph theory and their comparison, queuing theory, Petri nets, evolution algorithms.
Specification of controlled education, way of implementation and compensation for absences
Not applicable.
Recommended optional programme components
Not applicable.
Prerequisites and corequisites
Not applicable.
Basic literature
Goldreich, Oded. "Computational complexity: a conceptual perspective." ACM SIGACT News 39.3 (2008): 35-39. (EN)
Recommended reading
Bürgisser, Peter, Michael Clausen, and Amin Shokrollahi. Algebraic complexity theory. Vol. 315. Springer Science & Business Media, 2013. (EN)
Mitleton-Kelly, Eve. Complex systems and evolutionary perspectives on organisations: the application of complexity theory to organisations. Elsevier Science Ltd, 2003. (EN)
Mitleton-Kelly, Eve. Complex systems and evolutionary perspectives on organisations: the application of complexity theory to organisations. Elsevier Science Ltd, 2003. (EN)
Elearning
eLearning: currently opened course
Classification of course in study plans
Type of course unit
Elearning
eLearning: currently opened course