The recommended literature is determined individually in accordance with the topic of the diploma thesis.
The diploma thesis is the result of the student's own work. It must not show elements of academic fraud and must meet the criteria of good research practice defined in the Rector's Decision no. 21/2021, which lays down the rules for assessing plagiarism at Pavol Jozef Šafárik University in Košice and its components. Fulfillment of the criteria is verified mainly in the process of supervision and in the process of thesis defense. Failure to do so is reason for disciplinary action.
Slovak and optionally English.
1. Elaboration of the diploma thesis in accordance with the instructions of the supervisor.
2, Presentation of the results of the diploma thesis before the examination commission.
3. Answering questions related to the topic of the diploma thesis within the discussion.
The diploma thesis demonstrates mastery of extended theory and professional terminology of the field of study, acquisition of knowledge, skills and competencies in accordance with the declared profile of the graduate of the study program, as well as the ability to apply them creatively in solving selected field problems. Student demonstrates the ability of independent professional work in terms of content, formal and ethical. Further details on the diploma thesis are determined by Directive no. 1/2011 on the basic requirements of final theses and the Study Regulations of UPJŠ in Košice for the 1st, 2nd and combined 1st and 2nd degree.
The recommended literature is determined individually in accordance with the topic of the diploma thesis.
The diploma thesis is the result of the student's own work. It must not show elements of academic fraud and must meet the criteria of good research practice defined in the Rector's Decision no. 21/2021, which lays down the rules for assessing plagiarism at Pavol Jozef Šafárik University in Košice and its components. Fulfillment of the criteria is verified mainly in the process of supervision and in the process of thesis defense. Failure to do so is reason for disciplinary action.
Slovak
1. Elaboration of the diploma thesis in accordance with the instructions of the supervisor.
2. Presentation of the results of the diploma thesis before the examination commission.
3. Answering questions related to the topic of the diploma thesis within the discussion.
The diploma thesis demonstrates mastery of extended theory and professional terminology of the field of study, acquisition of knowledge, skills and competencies in accordance with the declared profile of the graduate of the study program, as well as the ability to apply them creatively in solving selected field problems. Student demonstrates the ability of independent professional work in terms of content, formal and ethical. Further details on the diploma thesis are determined by Directive no. 1/2011 on the basic requirements of final theses and the Study Regulations of UPJŠ in Košice.
Information sources recommended within individual profile subjects.
ZNA01 Basic concepts of Formal Concept Analysis.
ZNA02 Basic algorithms of Formal Concept Analysis.
NEU03 Feedforward neural networks and the backpropagation method.
NEU04 Convolution and Convolutional Neural Networks.
NEU05 Deep Neural Networks (DNN) and their use.
NEU06 Graph Neural Networks (GNN) and their applications.
STU07 Phases of machine learning, overfitting of machine learning models.
STU08 Decision trees, random forests, and other types of ensemble models of machine learning.
STU09 Support Vector Machines (SVM) and other classification methods.
ZNA10 Basic concepts of fuzzy logic and their use in formal concept analysis.
VSM11 Solving systems of linear equations, calculating eigenvalues and eigenvectors of matrices.
VSM12 General methods of generating random numbers.
VSM13 Stochastic processes and the MCMC method.
VSM14 Principles and methods of cluster analysis.
VSM15 Principal Component Analysis (PCA), Factor Analysis.
KOA16 Polynomial and NP-complete problems of combinatorial optimization.
KOA17 Optimal acyclic subgraphs (paths, trees, matchings).
KOA18 Network flows (algorithms, complexity, modifications, and applications).
NPR16 Stochastic processes and their properties: Concepts of stationarity, causality, invertibility, and the time-domain approach.
NPR17 Time series analysis in the frequency domain: Spectral density and the periodogram.
NPR18 Time series prediction: Properties of predictors and forecasting models.
Appropriate knowledge and competencies from the profile subjects of the study program, demonstrating the ability to synthesize the acquired knowledge and procedures and apply them to the problems of data analysis and artificial intelligence.
Slovak language or English language
ZNA01 Basic concepts of Formal Concept Analysis.
ZNA02 Basic algorithms of Formal Concept Analysis.
NEU03 Feedforward neural networks and the backpropagation method.
NEU04 Convolution and Convolutional Neural Networks.
NEU05 Deep Neural Networks (DNN) and their use.
NEU06 Graph Neural Networks (GNN) and their applications.
STU07 Phases of machine learning, overfitting of machine learning models.
STU08 Decision trees, random forests, and other types of ensemble models of machine learning.
STU09 Support Vector Machines (SVM) and other classification methods.
ZNA10 Basic concepts of fuzzy logic and their use in formal concept analysis.
VSM11 Solving systems of linear equations, calculating eigenvalues and eigenvectors of matrices.
VSM12 General methods of generating random numbers.
VSM13 Stochastic processes and the MCMC method.
VSM14 Principles and methods of cluster analysis.
VSM15 Principal Component Analysis (PCA), Factor Analysis.
KOA16 Polynomial and NP-complete problems of combinatorial optimization.
KOA17 Optimal acyclic subgraphs (paths, trees, matchings).
KOA18 Network flows (algorithms, complexity, modifications, and applications).
NPR16 Stochastic processes and their properties: Concepts of stationarity, causality, invertibility, and the time-domain approach.
NPR17 Time series analysis in the frequency domain: Spectral density and the periodogram.
NPR18 Time series prediction: Properties of predictors and forecasting models.
Verification of acquired student competencies in accordance with the graduate profile.