P.Kaprálik, J.Galanová, M.Polakovič: Logické systémy, STU 2009 ISBN 978-80-227-3205-5

J. M. Bernard, J. Hugon: Od logických obvodu k mikroprocesorum I- IV SNTL, Praha 1984

Alan Clements: Principles of Computer Hardware, Oxford University Press, Fourth edition 2006, ISBN 0-19-927313-8

Logické systémy / Norbert Frištacký ... [et al.]. Bratislava : Alfa, 1990

Continuing evaluation: tests

At least 50% aggregate score from tests is required for the admission to the exam.

Exam: exam

Indicative grading scheme: A 90%, B 80%, C 72%, D 65%, E 60%

Supplementary classification in case of repair test - oral exam is possible.

Slovak, English

Principles of circuits DDL, DTL and TTL

Boolean functions - combinatorial logical networks and their optimization

Selected combinatorial curcuits (adder, multiplexer, demultiplexer)

Physical implementation of automata (sequential circuits and their applications)

RS and D flip-flops, counter, shift register, three-state output, memory control R/W, parallel code converter, RS232 interface, static and dynamic RAMs and their organization

Microprocessors

Microprocessor structure

Arithmetic-logic unit (ALU) structure

Implementation of instructions and their classification

Addressing methods, internal and external memory

Auxiliary devices, their connection and data transfer, bus

Implementation of coupling circuits for memory and peripherals

Characteristics affecting computer performance, clock fequency, access time, transmission capacity, register size

Programming of single-chip microcomputers in higher and low-level programming language

Fundamentals of robotics, types of signals, their processing and control

http://davinci.fmph.uniba.sk/~gregus2/PPH/index.html

or MOODLE

70/30

Students acquire essential knowledge of principles and properties of basic digital circuits. Gain practical experience to design and construct digital electronic devices and programing selected microprocessors and simple robotic systems.

Biological sequence analysis : Probabilistic models of proteins and nucleic acids / Richard Durbin ... [et al.]. Cambridge : Cambridge University Press, 1998

Understanding bioinformatics / Marketa Zvelebil, Jeremy O. Baum. New York : Garland Science, 2008

Homework assignments (30%), group project (10%), weekly quizzes (10%), written exam (50%). Grades: A 90%, B 80%, C 70%, D 60%, E 50%. More information on the course website.

Slovak, English

Basic concepts from molecular biology, algorithms and machine learning. Sequencing and assembling genomes. Gene finding. Sequence alignment. Evolutionary models and phylogenetic trees. Comparative and population genomics. RNA structure. Motif finding and gene expression analysis. Protein structure and function. Selected current topics.

Students of computer science programs will focus on computer science methods and mathematical modeling of the covered problems.

https://fmfi-compbio.github.io/mbi/

50/50

Students will be familiar with basic problems and methods in bioinformatics; they will be able to choose an appropriate method for a given biological problem and to interpret its results.

Lepš, J., Šmilauer, P. (2016). Biostatistika. Episteme, Nakladatelství JU, České Budějovice, 438 s. ISBN: 978-80-7394-587-9

Waczulíková, I., Slezák, P. (2015). Introductory Biostatistics. Bratislava: Comenius University, 1st Edition. 147 p. ISBN 978-80-223-3938-4.

Somorčík, J., Teplička, I. (2015). Štatistika zrozumiteľne. Bratislava : Enigma, 1. vydanie, 2015, 244 s. ISBN 9788081330421.

Zvárová J. (2011). Základy statistiky pro biomedicínské obory. Praha : Karolinum. 218 p. ISBN 80-7184-786-0

Motulsky, H. (2014). Intuitive Biostatistics. New York : Oxford University Press, 3rd Edition, 2014, 540 p. ISBN 987-0-19-994664-8.

-

Preliminary evaluation: homeworks

Final exam: oral / written written and oral exam

Indicative evaluation scale: A 90%, B 80%, C 70%, D 60%, E 50%

Basic principles of scientific research methodology. Probability, random variable and its characteristics, types of probability distributions, basic concepts of applied statistics. Data collection, cleaning, sorting and coding, types, scales of variables, descriptive statistics, summarisation and presentation of data. Point and interval estimates, the concept of statistical hypothesis testing, P-value, interpretation of hypothesis testing results, statistical and biological significance. Analysis of categorical data, proportions, contingency tables, case classification, diagnostic tests. Comparison of sample means, t-tests. Analysis of variance. Nonparametric methods. Correlation and simple linear regression. Introduction to multivariable analysis. Practical part: solving real biomedical problems using statistical software Statsdirect and MS Excel with an add-in programmed in MS Visual Basic® for Application (VBA).

20/80

On completion of the course, students will gain knowledge of the basic principles of scientific research methodology - they will be able to identify and apply different research types, and research designs. They will master the most commonly used statistical methods for biological and clinical data analysis and be able to work with statistical software and Excel add-ins.

Discrete and combinatorial mathematics : An applied introduction / Ralph Grimaldi

Harlow : Pearson Education, 2014

Continuous assessment: assessed homework, tests, written exams At least

50% of the points from the continuous assessment are required for

admission to the examination. Exam: written, oral Exam weight: 50%

Slovak, English

Introduction to combinatorics and enumeration, basic combinatorial

configurations, enumeration principles, basic combinatorial identities.

Pascal's formula, binomial and polynomial theorem. Intuitive set theory

and its paradoxes, basic set relations and operations on sets,

cardinality of sets. Propositional logic, quantified propositions,

basic methods of mathematical proofs. The principle of inclusion and exclusion and an introduction to discrete probability.

Relations and representations of relations (sets, graphs, matrices). Significant relations (equivalence, partial ordering). Graph theory, basic concepts and selected algorithms.

50/50

This course will provide the mathematical foundations necessary for the

study of computer science. Students will also learn mathematical

culture, ways of thinking, reasoning and arguing in mathematics, as

well as proof techniques. Specifically, the course will cover basic

concepts, methods and algorithms from combinatorics, set theory, graph

theory and logic.

K.A. Stroud, D.J Booth, Engineering Mathematics, Industrial Press Inc. New York, 7th Edition

Continuous assessment:

i) short control tests (5-10 short test during discussion sessions) - together approx. 30% of total course points,

ii) midterm exam - approx. 30% of total course points

At least 30% of the points from the maximum of the continuous assessment are required to be admitted to the final exam.

Final examination: together approx. 40% of total course points, consists of written test and oral exam with equal weights

Slovak, English

Review of artimetics, algebraic operations, basic functions, trigonometry. Functions. Trigonometric, exponential, logarithmic and hyperbolic functions. Rational functions and limits. Complex numbers. Derivatives and their application. Series. Power series. Taylor expansion and approximation of functions. Plane curves and their fitting. Definite and indefinite integral and their applications.

60/40

In this course, students review their basic arithmetic skills and graphical representation of common functions. After completing it, students will be able to individually use the basic tools of mathematical analysis (elementary functions, complex numbers, derivatives and integrals) and gain knowledge about their applications and interpretation in real life.

Computer networks / Andrew S. Tanenbaum. Upper Saddle River : Prentice-Hall, 2003

Operating systems : Internals and design principles / William Stallings. Upper Saddle River : Pearson/Prentice Hall, 2005

Teachers’ own electronic texts published on the course’s web page.

Activity during lectures and exercises (50%), written test (50%). Activity during lectures and exercises (50%), written test (50%)

Approximate grading scale: A 90%, B 80%, C 70%, D 60%, E 50%

Slovak, English

Basic concepts of computer systems (processor, memory, input-output devices).

Basic concepts of operating systems, processes, memory management, input and output management, file systems, access control, virtualization.

Basic concepts of computer networks, reference model OSI, TCP/IP, e-mail and web, security aspects.

Basics of using the command line in the Linux operating system. Users, groups, redirection, searching. File and folder management. Basic tools (vim, awk, sed, bash scripts).

http://www.dcs.fmph.uniba.sk/osps/

100/0

After completing the course, students will have an overview of the principles of computer operation, the tasks and principles of operating systems and the principles of operation and secure use of computer networks and their services. They will also gain command line skills on the Linux operating system.

Lineárna algebra a geometria I / Július Korbaš. Bratislava : Univerzita Komenského, 2003

Lineárna algebra a geometria : Cesta z troch rozmerov s presahmi do príbuzných odborov / Pavol Zlatoš. Bratislava : Albert Marenčin, 2011

Applied linear algebra and matrix analysis / Thomas S. Shores. New York : Springer, 2007

Linear algebra done right / Sheldon Axler. New York : Springer, 1997

Ongoing evaluation during the semester: quizzes, homeworks, midterms

Final exam: written and oral

Approximative grade scale: A 90%, B 80%, C 70%, D 60%, E 50%

Slovak, English

Systems of linear equations and the corresponding geometry, Gauss-Jordan elimination, matrix notation, vectors, matrix operations, inverse matrix, vector spaces, solutions spaces of homogeneous systems and their bases, linear dependence/independence, basis, dimension, rank of a matrix, linera transformations, kernel, image, scalar product, orthogonality, orthogonal projections, angle between vectors, orthogonal complement, Gram-Schmidt orthonormalization, determinant, characteristic polynomial, eigenvectors and eigenvalues, Laplace expansion, Cramer's rule, fileds, rings, permutations.

50/50

Presentation of the basic concepts of algebraic methods with focus on their use in informatics.

After completing the course, students will be familar with the fundamentals of linear algebra needed in informatics and computer graphics, including the algorithms: Matrices and matrix operations, linear dependence and independence, dimension of a vector space, determinants, the concept of a group, specific matrix groups, systems of linear equations and their solution, solution spaces, vector spaces, scalar product, orthogonal complement, linear transformtaions and their matrices, injectivity, surjectivity, linear and affine spaces, eigenvectors and eigenvalues

Computer graphics and image processing (in Slovak)/ Eugen Ružický, Andrej Ferko. Bratislava : Sapientia, 1995

BOHUMELOVÁ, M. ed. 2015. Rituál múzea v digitálnom veku. [online] https://issuu.com/sng.sk/docs/ritualmuzea_online/47

CAMERON, F. – KENDERDINE, S. eds. 2007. Theorizing Digital Cultural Heritage. ISBN 0-262-03353-4. Cambridge: MIT Press 2007

CROFTS, N. et al. eds. 2005. Definition of the CIDOC Conceptual Reference Model. http://cidoc.ics.forth.gr/official_release _cidoc.html

NATALE, M. T. et al. 2012. Handbook on virtual exhibitions and virtual performances. Roma: Officine grafische tiburtine.

Class materials available from class web page at http://www.sccg.sk/~ferko/Vdak.htm.

Continuous assessment: homework, 40%

Exam: midterm and final tests

Exam weight: 60%

Grading: A (100-91), B (90-81), C (80-71), D (70-61), E (60-51), Fx (50-0).

English, Slovak

The class is taught eventually in a distant mode, as well.

Basic definitions. Historical overview, concept of Visual Data Science. Metaphors of memory, data, metadata and paradata, international standard CIDOC CRM. Information and metadata visualization. International standardization, copyright, FAIR data.

Documentation. Original, copy, aura, genius loci (Benjamin, Norberg-Schulz, World Cultural Heritage), digital heritage theory (Cameron-Kenderdine). Image processing, digital and computational photography, best views, measuring image quality, e.g. copies of the object and the virtual museum (engagement factor). Bezděk 's methodology of photo documentation of Czech archives. Digitization of Slovak museums and galleries. CRUSE scanner and standard scanning modes. Scanning rare clean papers for experiments in the authoring tool Rebelle (Escape Motions). Point clouds, plane and space triangulation, levels of detail, capturing of reliefs and 3D objects.

Presentation of digital cultural heritage, PREMO standard, comparison of Europeana, Comeniana, Making Sense of Illustrated Handwritten Archives and Movio projects. Creation of a virtual museum. Europeana data model (EDM), encoding of image and multimedia data (TIFF, HDRI, MPEG-4, MPEG-7, MPEG-21, BigTable). Selected presentation methods (panoramas, tone mapping, Google Street View, Multi-view Stereo for Community Photo Collections, Digital Libraries, MOOC).

http://www.sccg.sk/~ferko/Vdak.htm

Weight of interim / final evaluation: Interim evaluation 40% / final 60%.

The course offers knowledge in the dynamically developing field of processing and presentation of large visual data of cultural heritage. Graduates will get acquainted with contactless scanning (Cruse), rules and methods of effective authoring and state-of-the-art technologies according to the recommendations of the ACM Computing Curriculum.

Continuing evaluation: homework assignments (50%), project (50%)

Indicative rating scale: A 90%, B 80%, C 70%, D 60%, E 50%

slovak, english

An introduction to the practical use of the Python programming language and the Pandas data processing library. The matplotlib library. Advanced plotting of graphs using the seaborn library (statistical distributions, confidence intervals, "heat maps", ...). Interactivity in data visualization using the Plotly library. Visualization of high-dimensional data. Methods for visualization of textual data. Data visualization in the context of machine learning.

Students will be able to analyze datasets of different sizes, pre-process them into a visualizable form as well as identify and practically apply different methods of data visualization using the Python programming language.

POUR, J. – MARYŠKA, M. – STANOVSKÁ, I. – ŠEDIVÁ, Z. 2018. Self Service Business Intelligence. Grada Publishing a. s. 2018.

LACKO, L. 2009. Business Intelligence v SQL Serveru 2008. Brno: Computer Press, 2009.

NOVOTNÝ O. – POUR J. – SLÁNSKÝ D. 2005. Business Intelligence. Jak využít bohatství ve vašich datech. Praha: Grada Publishing, 2005.

POUR, J. – NOVOTNÝ, O. – MARYŠKA, M. 2012. Business Intelligence v podnikové praxi. Professional Publishing 2012.

BEULEN, E. - DANS, M. A.: Data Analytics and Digital Transformation. Routledge New York, 2024.

HAERTZEN, D.: The Analytical Puzzle: Profitable Data Warehousing, Business Intelligence and Analytics Technics Publications, LLC; First edition, June 20, 2012.

HBR Guide to Data Analytics Basics for Managers Ebook + Tools, HBR 2018

TURBAN, E. – SHARDA, R. – ARNSSON, J. E. – KING, D. 2008. Business Intelligence: A Managerial Approach. Upper Saddle River: Pearson Prentice Hall, 2008.

TURBAN, E. – ARONSON, J. E. – LIANG, T. 2007. Decision Support and Business Intelligence Systems. Upper Saddle River: Pearson Prentice Hall, 2007.

Webové sídlo Akademickej knižnice UK – externé informačné zdroje prístupné pre UK: http://uniba.sk/o-univerzite/fakulty-a-dalsie-sucasti/akademicka-kniznica-uk/externe-informacne-zdroje/.

Ďalšie zdroje budú priebežne dopĺňané a aktualizované (vzhľadom na nové a dostupné zdroje).

https://www.selecthub.com/c/business-intelligence-tools

https://www.softwaretestinghelp.com/tools/25-important-business-intelligence-tools

Work during semester (2x test, project)

Grade assignment: A 91%, B 81%, C 73%, D 66%, E 60%

Slovak, English

The course is open only when at least 5 students sign up for the course.

The purpose of BI in the context of managerial work. Typical business problems, determination of indicators, metrics. Typical technical problems: heterogeneous systems, different data sources, non-uniform or inconsistent data models, data in time, data and information that are not directly in operational/transactional systems, different data quality in different systems, reporting limitations from operational/transactional systems, data in .xls(x) tables supplied by users.

Contingency table, reporting in a spreadsheet.

Transactional and analytical systems. Data, organization, facts and dimensions, data aggregation, data granularity.

Front-end analytical layer – Power BI, application window, Power Query editor, data sources.

Basic types of visualizations, their editing, saving, sharing, publishing, exporting.

Data loading, data transformation, data correction, basic data quality control.

Dimension creation, data model. Working with M.

Creating metrics, working with DAX

Merging and editing data sources (Merge and Append Queries), conditional columns

Analysis, drill down, drill lup, drill through.

Examples of specific solutions.

100/0

By completing the course, the student will gain knowledge and skills in using the Power BI tool, will be able to identify data sources, understand the ETL process, create a data model, and design a BI solution using the tool in a specific case. The student will present and explain their proposed solution.