Title:

Code:IAM
Ac.Year:2018/2019
Sem:Summer
Curriculums:
ProgrammeField/
Specialization
YearDuty
BIT-2ndElective
IT-BC-3BIT2ndElective
Language of Instruction:Czech
Credits:5
Completion:classified credit
Type of
instruction:
Hour/semLecturesSeminar
Exercises
Laboratory
Exercises
Computer
Exercises
Other
Hours:2618080
ExamsTestsExercisesLaboratoriesOther
Points:0505000
Guarantor:Holík Lukáš, Mgr., Ph.D. (DITS)
Deputy guarantor:Lengál Ondřej, Ing., Ph.D. (DITS)
Lecturer:Češka Milan, RNDr., Ph.D. (DITS)
Hliněná Dana, doc. RNDr., Ph.D. (DMAT)
Holík Lukáš, Mgr., Ph.D. (DITS)
Lengál Ondřej, Ing., Ph.D. (DITS)
Instructor:Češka Milan, RNDr., Ph.D. (DITS)
Hliněná Dana, doc. RNDr., Ph.D. (DMAT)
Holík Lukáš, Mgr., Ph.D. (DITS)
Lengál Ondřej, Ing., Ph.D. (DITS)
Faculty:Faculty of Information Technology BUT
Department:Department of Intelligent Systems FIT BUT
Prerequisites:
 Discrete Mathematics (IDA), DMAT Formal Languages and Compilers (IFJ), DIFS Numerical Methods and Probability (INM), DMAT

Learning objectives:

• Practice mathematical writing and thinking, formulation of problems and solving them,
• obtain deeper insight into several areas of mathematics with applications in computer science,
• learn on examples that complicated mathematics can lead to useful algorithms and tools.
Description:
The course is a follow-up to compulsory mathematical courses at FIT. Students learn how to use mathematic methods on several subjects closely related to computer science. These are mainly number theory and its application in cryptography, basic set theory and logic, logical systems and decision procedures with applications in e.g. databases or software engineering, probability, statistics, and their applications in analysis of probabilistic systems and artificial intelligence.
Knowledge and skills required for the course:
Basic knowledge of sets, relations, propositional and predicate logic, algebra, and finite automata.
Subject specific learning outcomes and competencies:
The ability to exactly and formally specify and solve problems, formally prove claims; also better understanding of the basic mathematical concepts, overview of several areas of mathematics important in computer science.
Generic learning outcomes and competencies:
Improving the abilities of exact thinking, expressing ideas, and using a mathematical apparatus.
Why is the course taught:
The main goal is not to cover a given area of mathematics but to infect a student with an enthusiasm for the subject. The student will be given an opportunity to walk the way from abstract theory to its actual use in the real world, which might possibly convince him/her that thinking and expressing ideas with a help of formal mathematics makes sense. These skills will be exercised through solving problem assignments that should be interesting and sometimes demanding. Obtaining a better overview of several areas of mathematics important in informatics is an added benefit.
Syllabus of lectures:

1. Axioms of set theory, axiom of choice. Countable and uncountable sets, cardinal numbers. (Dana Hliněná)
2. Application of number theory in cryptography. (Dana Hliněná)
3. Number theory: prime numbers, Fermat's little theorem, Euler's function. (Dana Hliněná)
4. Propositional logic. Syntax and semantics. Proof techniques for propositional logic: syntax tables, natural deduction, resolution. (Ondřej Lengál)
5. Predicate logic. Syntax and semantics. Proof techniques for predicate logic: semantic tables, natural deduction. (Ondřej Lengál)
6. Predicate logic. Craig interpolation. Important theories. Undecidability. Higher order logics. (Ondřej Lengál)
7. Hoare logic. Precondition, postcondition. Invariant. Deductive verification of programs. (Ondřej Lengál)
8. Decision procedures in logic: Classical decision procedures for arithmetics over integers and over rationals. (Lukáš Holík)
9. Automata-based decision procedures for arithmetics and for WS1S (Lukáš Holík)
10. Decision procedures for combined theories. (Lukáš Holík)
11. Advanced combinatorics: inclusion and exclusion, Dirichlet's principle, chosen combinatorial theorems. (Milan Češka)
12. Conditional probability, statistical inference, Bayesian networks. (Milan Češka)
13. Probabilistic processes: Markov and Poisson process. Applications in informatics: quantitative analysis, performance analysis.
Syllabus of numerical exercises:

1. Proofs in set theory, Cantor's diagonalization, matching, Hilbert's hotel.
2. Prime numbers and cryptography, RSA, DSA, cyphers.
3. Proofs in number theory, Chinese reminder theorem.
4. Proofs in propositional logic.
5. Proofs in predicate logic.
6. Decision procedures.
7. Computer labs 1.
8. Computer labs 2.
9. Automata decision procedures and combination theories.
10. Computer labs 3.
11. Proofs in combinatorics.
12. Conditional probability and statistical inference in practice.
13. Computer labs 4.
Syllabus of computer exercises:

1. Proving programs corrects in VCC.
2. SAT and SMT solvers.
3. Tools MONA and Vampire.
4. Analysis of probabilistic systems, PRISM.
Fundamental literature:

• A.R. Bradley, Z. Manna. The Calculus of Computation. Springer, 2007.
• D. P. Bertsekas, J. N. Tsitsiklis. Introduction to Probability, Athena, 2008. Scientific
• M. Huth, M. Ryan. Logic in Computer Science. Modelling and Reasoning about Systems. Cambridge University Press, 2004.
Study literature:

• R. Smullyan. First-Order Logic. Dover, 1995.
• B. Balcar, P. Štěpánek. Teorie množin. Academia, 2005.
• 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
• J. Hromkovič. Algorithmic adventures: from knowledge to magic. Dordrecht: Springer, 2009.
• Steven Roman. Lattices and Ordered Sets, Springer-Verlag New York, 2008.
• A. Doxiadis, C. Papadimitriou. Logicomix: An Epic Search for Truth. Bloomsbury, 2009.