P. J. Šafárik University in Košice - Faculty of Science P. J. Šafárik University in Košice Faculty of Science PF UPJŠ 2026/2027 14690074 S BSA ÚMV/BSA/22 Methods of data analysis and artificial intelligence 4 State examination course 26.02.2026 26.03.2026 ÚMV/FRPb/19 and ÚMV/LCO/10 and ÚMV/MST/19 and ÚINF/USU/19 and ÚINF/UNS1/15 ÚMV/FRPb/19 - Function of real variables and ÚMV/LCO/10 - Linear and integer programming and ÚMV/MST/19 - Mathematical statistics and ÚINF/USU/19 - Introduction to machine learning and ÚINF/UNS1/15 - Introduction to neural networks FRPb - Function of real variables and LCO - Linear and integer programming and MST - Mathematical statistics and USU - Introduction to machine learning and UNS1 - Introduction to neural networks 8 Person responsible for the delivery, development and quality of the study programme doc. Mgr. Jozef Kiseľák, PhD. 14687175 ÚMV/FRPb/19 FRPb Function of real variables , 14676280 ÚMV/LCO/10 LCO Linear and integer programming , 14687205 ÚMV/MST/19 MST Mathematical statistics , 14687218 ÚINF/USU/19 USU Introduction to machine learning , 14681929 ÚINF/UNS1/15 UNS1 Introduction to neural networks A 1259 ADUIb Data Science and Artificial Intelligence -1 present I. present true I. <_PA_> Conditions for completion of course

Knowledge and competencies from the profile subjects of the study program, demonstrating the ability to synthesize the acquired knowledge and procedures, and applying them to the problems of data analysis and artificial intelligence.

 <_PJ_> Language, which knowledge is needed to pass the course

slovak

 <_SO_> Brief outline of the course

The state examination is performed in a form of a debate with the emphasis on one topic of the following courses:ÚMV/FRPb/19, ÚMV/LCO/10, ÚMV/MST/19, ÚINF/USU/19 and ÚINF/UNS1/15.

1. Differential calculus, integral calculus and their applications.

2. Linear programming problems, solution methods and complexity, duality in linear programming and its interpretation.

3. Random variables, their distributions and characteristics, estimation theory and statistical hypotheses testing.

4. Basic principles of machine learning and its methods.

5. Basic principles of neural networks and their methods.

 <_VV_> Learning outcomes

Evaluation of student‘s competences with respect to the profile of the graduate.

 A 7 41.18 B 5 29.41 C 5 29.41 D 0 0.0 E 0 0.0 FX 0 0.0 17 6