Course detail

Selected Chapters on Mathematics

FIT-MADAcad. year: 2025/2026

The course extends undergrad mathematical courses. Mathematical thinking is demonstrated together with broadening and deepening knowledge of the areas of mathematics and their connection to applications in computer science is shown. The particular areas are, e.g., logics, proof techniques, decision procedures, formal model theory, lattices, probability, and statistics.

Doctoral state exam topics:

  1. Advanced finite automata methods. 
  2. Automata techniques in decision procedures and verification. 
  3. SAT and SMT techniques.
  4. Proof techniques in predicate and first-order logic.
  5. Logical decision procedures.
  6. Galois connection, abstract interpretation, and applications.
  7. Modal and temporal logics.
  8. Advanced probability theory.
  9. Stochastic process and their analysis.
  10. Markov decision processes and reinforcement learning. 
  11. Probabilistic programming and inference.
  12. Advanced graph algorithms. 
  13. Randomized algorithms.
  14. Process algebras.

Language of instruction

Czech

Mode of study

Not applicable.

Guarantor

doc. RNDr. Milan Češka, Ph.D.

Department

Department of Intelligent Systems (UITS)

Entry knowledge

Basic notions of relations, sets, propositional and first-order logic, algebra, finite automata.

Rules for evaluation and completion of the course

An exam at the end of the semester.

Aims

  • Provide PhD students with better knowledge of mathematical methods used in computer science, especially in formal methods, with the focus on the particular topic of the dissertation,
  • Deepen the skills of application of the mathematical apparatus in general.


The ability to formalize and solve problems using mathematical apparatus, in particular proving of theorems, deepening and practicing basic mathematical terms, overview of areas of mathematics with important applications in computer science, especially in those related to the topic of the dissertation.
Broadening the ability to precisely formalize concepts and use the mathematical apparatus.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Not applicable.

Recommended reading

B. Balcar, P. Štěpánek. Teorie množin. Academia, 2005.
Biere, A., Heule, M., Van Maaren, H., Walsh, T. Handbook of Satisfiability, IOS Press, 2009
C. M. Grinstead, J. L. Snell. Introduction to probability. American Mathematical Soc., 2012.
G. Chartrand, A. D. Polimeni, P. Zhang. Mathematical Proofs: A Transition to Advanced Mathematics, 2013
Christel Baier and Joost-Pieter Katoen: Principles of Model Checking, MIT Press, 2008. ISBN: 978-0-262-02649-9
J. Hromkovič. Algorithmic adventures: from knowledge to magic. Dordrecht: Springer, 2009.
M. Huth, M. Ryan. Logic in Computer Science. Modelling and Reasoning about Systems. Cambridge University Press, 2004.
Steven Roman. Lattices and Ordered Sets, Springer-Verlag New York, 2008.

Classification of course in study plans

  • Programme DIT Doctoral 0 year of study, summer semester, compulsory-optional
  • Programme DIT Doctoral 0 year of study, summer semester, compulsory-optional
  • Programme DIT-EN Doctoral 0 year of study, summer semester, compulsory-optional
  • Programme DIT-EN Doctoral 0 year of study, summer semester, compulsory-optional

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

prof. Ing. Tomáš Vojnar, Ph.D.
doc. RNDr. Milan Češka, Ph.D.
doc. Mgr. Adam Rogalewicz, Ph.D.
doc. Mgr. Lukáš Holík, Ph.D.
Ing. Ondřej Lengál, Ph.D.

Syllabus

  1. Advanced finite automata methods. 
  2. Automata techniques in decision procedures and verification. 
  3. SAT and SMT techniques.
  4. Proof techniques in predicate and first-order logic.
  5. Logical decision procedures.
  6. Galois connection, abstract interpretation, and applications.
  7. Modal and temporal logics.
  8. Advanced probability theory.
  9. Stochastic process and their analysis.
  10. Probabilistic programming and inference.
  11. Advanced graph algorithms. 
  12. Randomized algorithms.
  13. Process algebras.

Guided consultation in combined form of studies

26 hod., optionally

Teacher / Lecturer

doc. RNDr. Milan Češka, Ph.D.