Course detail

Advanced Theoretical Informatics

FEKT-VPTIAcad. year: 2018/2019

Complexity theory, graph theory, graph equivalence, queuing theory, Petri nets, simulation and modeling, Markov models, advanced evolutionary algorithms.

Learning outcomes of the course unit

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.

Prerequisites

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

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. (CS)
Mitleton-Kelly, Eve. Complex systems and evolutionary perspectives on organisations: the application of complexity theory to organisations. Elsevier Science Ltd, 2003. (CS)
Bürgisser, Peter, Michael Clausen, and Amin Shokrollahi. Algebraic complexity theory. Vol. 315. Springer Science & Business Media, 2013. (CS)

Planned learning activities and teaching methods

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.

Assesment methods and criteria linked to learning outcomes

final examination

Language of instruction

Czech

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
14. Final exam

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

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.

Classification of course in study plans

  • Programme IBEP-V Master's

    branch V-IBP , 1. year of study, winter semester, 5 credits, compulsory

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Computer exercise

26 hours, compulsory

Teacher / Lecturer