Czech

Not applicable.

Alumni know complexity theory, representative examples and are able to apply graph theory, queue theory, theory of Petri nets and Markov models to solve the selected examples.

The subject knowledge on the heoretical informatics, t Bachelor degree and courlevel is required.

Not applicable.

Teachning methods include lectures, computer laboratories and practical laboratories. Course is taking advantage of e-learning (Moodle) system. Students have to write a single project/assignment during the course.

final examination

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

Not applicable.

Objective of this course is to provide information about complexity theeory, graph theory and their comparison, queuing theory, Petri nets, evolution algorithms.

The content and forms of instruction in the evaluated course are specified by a regulation issued by the lecturer responsible for the course and updated for every academic year.

Not applicable.

Not applicable.

GOODFELLOW, I., BENGIO, Y., & COURVILLEe, A. (2016). Deep learning (adaptive computation and machine learning series). Adaptive Computation and Machine Learning series, 800. (EN)
Virius, Miroslav. Základy algoritmizace. Česká technika-nakladatelství ČVUT, 2008. (CS)

Not applicable.