The sources of information depend on the selected project.

Active participation in the project. Participating in regular project team meetings. Presentation of the results achieved in solving a specific problem. Uploading a software work. Preparation of materials for the promotion of the final work.

Slovak or english

Content prerequisities:

advanced programming skills

The course is realized as part of "Živé projekty" (Live projects) in cooperation with the Technical University of Košice and several software companies. Students work in a team of 4-5 members to develop, test and present a software product under the guidance of a mentor from a university or a software company.

1. Team creation and project selection takes place at the beginning of October

2. Students meet with the project mentor on a weekly basis and continuously work on the creation of a software product

3. Around mid-January, students submit a video with a short presentation of the project

4. At the beginning of February, the project presentation takes place. The best teams are awarded with material prizes.

Learn how to work on a larger software part at all stages of its life cycle. Be able to analyze and explicitly express user requirements, precisely specify the task, design a solution and evaluate alternatives. Implement and test an effective and correctly designed solution. Learn to keep detailed documentation and present the results of the work in writing and in public. Learn to work together in a development team, share work effectively and exchange ideas.

[1] Watt, J., Borhani, R., Katsaggelos, A.K.: Machine learning refined: foundations, algorithms, and applications. Cambridge: Cambridge University Press, 2016.

[2] Zhao, Y., Cen, Y.: Data Mining Applications with R. Elsevier Inc. 2014.

[3] Han, J. and Kamber, M.: Data Mining Concepts and Techniques. 3rd Edition, Morgan Kaufmann, Burlington, 2011.

[4] Witten, I.E., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, Elsevier, 2005.

The realization of a project focused on case studies in data mining.

Successful completion of the written and oral part of the exam focused on case studies in data mining.

Slovak or English

1. Basic notions in data mining

2. Data preparation in data mining

3. Methods and algorithms of data mining

4. Methods and algorithms of data mining II

5. Extraction of knowledge from large data volumes

6. Case study analysis using data mining methods in different application areas

7. Case study analysis using data mining methods in different application areas II

8. Application of methods for automated analysis of large data volumes

9. Solving practical tasks using appropriate software tools

10. Solving practical tasks using appropriate software tools II

11. Solving practical tasks using appropriate software tools III

12. Testing data mining algorithms

13. Testing data mining algorithms II

Solving practical tasks in the field of data mining. Basic concepts of data mining. Knowledge of data mining methods.

1. BRIDGES, Douglas. Computability, A Mathematical Sketch book. Springer--Verlag, 1994. ISBN:: 978-0387941745

2. BUKOVSKÝ, Lev. Teória algoritmov, ES UPJŠ, Košice, 1999. ISBN 8070973730

3. MACHTEY, Michael a Paul YOUNG. An Introduction to the General Theory of Algorithms, North--Holland, Amsterdam 1978.

4. KRAJČI, Stanislav. Teória vypočítateľnosti. http://ics.upjs.sk/~krajci/skola/vyucba/ucebneTexty/vypocitatelnost.pdf

Two written examinations focused on the construction of Turing machines, creating sequences of (primitive) recursive functions, solving examples. Oral exam focused on the relationship between classes of recursive and computable functions, the problem of stopping a Turing machine.

Slovak

1. Turing machine, basic principles of work of Turing machine, formalization of basic notions

2. Shifting of states, compositions of machines, computations on composed machines

3. Modifications of configuration

4. Elementary Turing machines

5. Compositions of elementary Turing machines

6. Primitively recursive functions

7. Primitively recursive predicates

8. Functions and predicates from number theory

9. Goedelian arithmetizationa of Turing computability

10. Recursive functions

11. Relationship of recursivity and Turing computability

12. Halting problem

https://ics.upjs.sk/~krajci/skola/vyucba/jesen/predmety/TVY.html

Knowledge of computational model of Turing machine, Goedelian arithmetization, and relationship between Turing computability and recursivity of functions.

1. D. E. Knuth, The TeXbook, Computers and Typesetting, Addison-Wesley, Reading, Massachusetts, 1986.

2. M. Doob, Jemný úvod do TeXu, CSTUG, 1990; èeský preklad z "A Gentle Introduction to TeX” (text vo¾ne prístupný v CTAN archíve).

3. O. Ulrych, AMS-TeX za 59 minút, (verzia 1.0), Praha, 1989.

4. J. Chlebíková, AMS-TeX (verzia 2.0), Bratislava, 1992.

5. M. Spivak, The Joy of TeX, Amer. Math. Soc., 1986.

6. L. Lamport, LaTeX: A Document Preparation System, Addison-Wesley, Massachusetts, 1986.

7. L. Lamport, MakeIndex: An index processor for LaTeX, 17 February 1987.

8. J. Rybièka, LaTeX pro začátečníky, Konvoj, Brno, 1995.

9. H. Partl, E. Schlegl, I. Hyna, P. Sýkora, LaTeX – Stručný popis.

10. T. Oetiker, H. Partl, I. Hyna, E. Schlegl, M. Kocer, P. Sýkora, Ne příliš stručný úvod do systému LaTeX2e (neboli LaTeX2e v 73 minutách).

11. M. Goossens, F. Mittelbach, and A. Samarin, The LaTeX Companion, Addison-Wesley, Reading, Massachusetts, 1994. Kapitola 8 je volne prístupná v TeX archívoch (ch8.pdf). 4

12. G. Grätzer, Math into LaTeX, 3rd edition, Birkhäuser, Boston, 2000.

Satisfiable ability to correct mainly mathematical typesetting.

Slovak.

1. Principles for typesetting of documents containing mathematical formulas.

2. Typesetting of a plain text, special text symbols, using of text fonts.3

3. TeX macros.

4. Enumerations in text and footnote command. Parameter setting determining the appearance of the pages.

5. Typesetting of mathematical formulas in text and displays, aligning formulas.

6. Making tables and pictures.

7. Definitions, theorems, and proofs in a mathematical document.

8. Contents, bibliography, sections in a document.

9. Pictures.

10.-12. Project.

https://ics.upjs.sk/~krajci/skola/vyucba/jar/predmety/tex/tex.html

To provide the basic information on principles for typesetting of documents containing mathematical formulas.

1. AGGARWAL, Charu C. Neural networks and deep learning: a textbook. Cham: Springer, 2018. ISBN 978-3319944623.

2. KVASNIČKA, Vladimír. Úvod do teórie neurónových sietí. [Slovenská republika]: IRIS, 1997. ISBN 80-88778-30-1.

3. KVASNIČKA, Vladimír. Evolučné algoritmy. Bratislava: Vydavateľstvo STU, 2000. Edícia vysokoškolských učebníc. ISBN 80-227-1377-5.

4. MITCHEL, Melanie. An Introduction to Genetic Algorithms. Cambridge: MIT Press, 2002. ISBN 0-262-63185-7.

5. SINČÁK, Peter, ANDREJKOVÁ, G. Úvod do neurónových sietí, I. diel, Košice: ELFA, 1996. ISBN 808878638X

The condition for passing the course is the realization of a project with the application of neural networks, successful completion of two written tests in the field of neural networks, their basic types, and genetic algorithms, as well as successful completion of the written and oral part of the exam.

Slovak or English

Content prerequisites:

Basics of programming in Python, or another alternative programming language suitable for data analysis

1. Basic concept arising from biology. Linear threshold units, polynomial threshold units, functions calculable by threshold units.

2. Perceptrons. Linear separable objects, adaptation process (learning), convergence of perceptron learning rule, higher order perceptrons.

3. Forward neural networks, hidden neurons, adaptation process (learning), backpropagation method.

4. Recurrent neural networks. Hopfield neural networks, properties, associative memory model, energy function, learning, optimization problems (business traveler problem).

5. Model of gradually created network. ART network, architecture, operations, initialization phase, recognition phase, search and adaptation phase. Use of the ART network.

6. Applications of studied models in solving practical problems.

7. Written test I.

8. Motivation to model genetic elements. Genetic algorithm. Application of genetic algorithms.

9. Genetic programming, root trees, Read's linear code. Basic stochastic optimization algorithms: blind algorithm and climbing algorithm. Forbidden search method.

10. Genetic and evolutionary programming with typing, examples of use. Grammatical evolution.

11. Special techniques of evolutionary computations. Selection mechanisms in evolutionary algorithms.

12. Use of genetic algorithms in training neural networks. Artificial life.

13. Written test II.

The result of the education is an understanding of the basic principles of neural networks and genetic algorithms. The student will gain the ability to apply the acquired knowledge in intelligent data analysis and also work with a selected tool for modeling neural networks.

1. J.E. Hopcroft, R.Motwani, J.D. Ullman: Introduction to automata theory, languages, and computation, Addison-Wesley, 2007.

2. M. Sipser: Introduction to the Theory of Computation, Thomson, 2nd edition, 2006.

3. L.A.Hemaspaandra, M.Ogihara: Complexity theory companion, EATCS series, texts in computer science, Springer-Verlag, 2002.

4. S. Arora, B. Barak: Computational Complexity: A Modern Approach, Cambridge Univ. Pess, 2009. 5. G.Brassard, P.Bradley: Fundamentals of algorithmics, Prentice Hall, 1996.

6. D.P.Bovet, P.Crescenzi: Introduction to the theory of complexity, Prentice Hall, 1994.

7. C. Calude and J. Hromkovič: Complexity: A Language-Theoretic Point of View, in G. Rozenberg and A. Salomaa, Handbook of Formal Languages II, Springer, 1997.

Oral examination.

Slovak or english

Content prerequisities:

Basic notions from the theory of automata and formal languages.

Basic skills in programming and design of algorithms (in any programming language).

Basics knowledge in mathematical logic, set theory, and graph theory.

1: Introduction: the notion of computational complexity, computational time, computational model, example - the problem of sorting, computational complexity as an asymptotic function

2: Basic computational models: RAM and RASP computers, the cost of an elementary step on these computers, single-tape Turing machine, multi-tape Turing machine, nondeterministic variants of these computational models, transformations among these models with respect to the time complexity

3: The classes P and NP: basic definitions, presenting (un)undirected graphs on the input, 3COL – the set of all 3-colorable graphs is in NP, 2COL - the set of all 2-colorable graphs is in P, SAT – the set of satisfiable Boolean formulas is in NP, CNF-SAT - Boolean formulas in conjunctive normal form

4: Variants of P and NP: decision problem, the problem of finding a solution, optimization problem, polynomial conversions among different variants

5: NP-completeness: reducibility in polynomial time and its transitivity, definition of the NP-completeness and its basic properties

6: NP-completeness of SAT

7: Variants of SAT: 3CNF-SAT - satisfiability of Boolean formulas in 3-conjunctive normal form, kCNF-SAT, CNF-SAT - satisfiability in k-conjunctive (conjunctive) normal form, 2CNF-SAT is in P

8: 3COL and its variants: 3COL (the problem of coloring vertices of a graph with 3 colors) in NP-complete, consequently: for each k>3, kCOL (the problem of coloring with k colors) is NP-complete as well

9: Colorability of a planar graph with three colors: presenting a planar graph on the input, the proof of NP-completeness, coloring with a larger number of colors

10: Another NP-complete problems: Exact set cover, Clique, Vertex cover

11: Hamiltonian path: Hamiltonian path in a directed and in undirected graph

12: Subset-sum-like problems: Subset Sum - the problem of whether any subset of the integers sum to precisely a target sum, Partition - the problem of whether a given multiset of positive integers can be partitioned into two subsets with equal sums, a “more relaxed” version of Partition - achieving an approximate equality of the sums, distribution of tasks among K parallel processors

13: Beyond P a NP: a review of the basic complexity classes - L, NL, P, NP, PSpace , NPSpace, ExpTime, NExpTime, …, simulation of (non)deterministic space in (non)deterministic time, conversions in opposite directions

14: PSpace: QBF - true quantified Boolean formulas, prenex normal form, Pspace completeness of QBF, PSpace = NPSpace

To give students theoretical background in computational complexity and theory of NP-completeness.

1. LPIC-1 Exam 101. LPI [online]. Canada: The Linux Professional Institute, 2021 [cit. 2021-9-22]. Dostupné z: https://learning.lpi.org/en/learning-materials/101-500/, 2. LPIC-1 Exam 102. LPI [online]. Canada: The Linux Professional Institute, 2021 [cit. 2021-9-22]. Dostupné z: https://learning.lpi.org/en/learning-materials/102-500/, 3. Linux - Dokumentační projekt [online]. 4. Praha: Computer Press, 2007 [cit. 2021-9-22]. Dostupné z: https://i.iinfo.cz/files/root/k/LDP_4.pdf.

The condition for passing the course is: 1. Homeworks (50% of the total number of points), 2. Written final theoretical exam (25% of the total number of points), 3. Written final practical exam (25% of the total number of points).

Slovak or English

1. Introduction to Unix/Linux systems, 2. Linux ommand line, 3. Text processing tools, 4. Managing files, 5. Managing users, groups and rights, 6. Managing processes, 7. Managing software and packages, 8. Administering the system - system booting, jobs, logging,9. Basic networking, 10. Managing network interfaces, 11. Managing disk partitions, 12. Exam.

https://csl.science.upjs.sk/#/predmety/

The result of the education is an understanding of the theoretical and practical background for studying computer science, by giving the necessary knowledge in the usage of Unix/Linux operating systems.