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A course is the basic teaching unit, it's design as a medium for a student to acquire comprehensive knowledge and skills indispensable in the given field. A course guarantor is responsible for the factual content of the course.
For each course, there is a department responsible for the course organisation. A person responsible for timetabling for a given department sets a time schedule of teaching and for each class, s/he assigns an instructor and/or an examiner.
Expected time consumption of the course is expressed by a course attribute extent of teaching. For example, extent = 2 +2 indicates two teaching hours of lectures and two teaching hours of seminar (lab) per week.
At the end of each semester, the course instructor has to evaluate the extent to which a student has acquired the expected knowledge and skills. The type of this evaluation is indicated by the attribute completion. So, a course can be completed by just an assessment ('pouze zápočet'), by a graded assessment ('klasifikovaný zápočet'), or by just an examination ('pouze zkouška') or by an assessment and examination ('zápočet a zkouška') .
The difficulty of a given course is evaluated by the amount of ECTS credits.
The course is in session (cf. teaching is going on) during a semester. Each course is offered either in the winter ('zimní') or summer ('letní') semester of an academic year. Exceptionally, a course might be offered in both semesters.
The list presented here is sorted by departments, within departments alphabetically by the Course title. The departments are listed alphabetically by the Department code.

Courses


18105 Department of Applied Mathematics
Course code Course title Extent of teaching
Completion
Semester
Credits
Guarantor Instructors
BI-ALO Algebra and Logic 2P+1C Z,ZK L 4 Starý J. Starý J.
BIE-ZUM Artificial Intelligence Fundamentals 2P+2C Z,ZK L 4 Surynek P. Surynek P.
BI-ZUM Artificial Intelligence Fundamentals 2P+2C Z,ZK L 4 Surynek P. Surynek P.
MI-UMI Artificial intelligence 2P+1C Z,ZK Z 5 Surynek P. Surynek P.
NI-UMI Artificial intelligence 2P+1C Z,ZK Z 5
MI-BML Bayesian Methods for Machine Learning 2P+1C KZ L 5 Dedecius K., Tichý O. Dedecius K., Tichý O.
NI-BML Bayesian Methods for Machine Learning 2P+1C KZ L 5
MI-VYC Computability 2P+2C Z,ZK L 4 Starý J. Starý J.
MI-MVI Computational Intelligence Methods 2P+1C Z,ZK Z 4
MI-MVI.16 Computational Intelligence Methods 2P+1C Z,ZK Z 5 Kordík P. Kordík P.
NI-MVI Computational Intelligence Methods 2P+1C Z,ZK Z 5
MI-ADM.16 Data Mining Algorithms 2P+1C Z,ZK L 5 Kordík P. Klouda K., Kordík P., Vašata D.
MI-ADM Data Mining Algorithms 2P+1C Z,ZK L 4
NI-ADM Data Mining Algorithms 2P+1C Z,ZK L 5
BIE-VZD Data Mining 2P+2C Z,ZK Z 4 Kordík P. Dedecius K.
BIK-VZD Data Mining 13KP+4KC Z,ZK L 4
BI-VZD Data Mining 2P+2C Z,ZK Z 4 Kordík P. Klouda K., Vašata D.
MI-PDD Data Preprocessing 2P+1C Z,ZK Z 4
MI-PDD.16 Data Preprocessing 2P+1C Z,ZK Z 5 Jiřina M. Jiřina M.
NI-PDD Data Preprocessing 2P+1C Z,ZK Z 5
MI-DDM Distributed Data Mining 3C KZ L 4 Borovička T. Borovička T., Stuchlík O.
BIE-ZMA Elements of Calculus 3P+2C Z,ZK Z 6 Kalvoda T. Rybníčková J.
BIK-ZMA Elements of Calculus 20KP+4KC Z,ZK Z 6 Kalvoda T. Petr I.
BI-ZMA Elements of Calculus 3P+2C Z,ZK Z 6 Kalvoda T. Hrabák P., Kalvoda T., Petr I.
BIE-ZDM Elements of Discrete Mathematics 2P+2C Z,ZK Z 5 Kolář J. Kolář J.
BIK-ZDM Elements of Discrete Mathematics 13KP+4KC Z,ZK Z 5 Kolář J. Pernecká E.
BI-ZDM Elements of Discrete Mathematics 2P+2C Z,ZK Z 5 Kolář J. Dombek D., Kolář J., Scholtzová J.
MI-EDW Enterprise Data Warehouse Systems 2P+1C Z,ZK L 4
MI-GLR Games and reinforcement learning 2P+2C Z,ZK L 4
BIE-HMI History of Mathematics and Informatics 2P+1C Z,ZK L 3 Šolcová A. Šolcová A.
BI-HMI History of Mathematics and Informatics 2P+1C Z,ZK L 3 Šolcová A. Šolcová A.
BIK-HMI History of Mathematics and Informatics 13KP+2KC ZK L 3 Šolcová A. Šolcová A.
MIE-HMI History of Mathematics and Informatics 2P+1C Z,ZK Z 3 Šolcová A. Šolcová A.
MI-HMI2 History of Mathematics and Informatics 2P+1C ZK Z 3 Šolcová A. Šolcová A.
MI-IKM Internet and Classification Methods 1P+1C Z,ZK L 4 Holeňa M. Holeňa M.
BI-UVM Introduction to higher mathematics 3+2 Z Z 6
BIE-IMA2 Introduction to Mathematics 2 1C Z Z 2
BIE-IMA Introduction to Mathematics 3C Z Z 4 Marchesiello A. Marchesiello A.
BIK-PKM Introduction to Mathematics Z Z 4
BI-PKM Introduction to mathematics Z Z 4 Klouda K.
BI-ZNS Knowledge-based Systems 2P+2C Z,ZK Z 5 Jiřina M. Jiřina M.
BIE-LIN Linear Algebra 4P+2C Z,ZK L 7 Klouda K. Hrabák P.
BIK-LIN Linear Algebra 26KP+4KC Z,ZK L 7 Klouda K. Klouda K.
BI-LIN Linear Algebra 4P+2C Z,ZK L 7 Dombek D. Dombek D., Kleprlík L.
BIE-MLO Mathematical Logic 2P+2C Z,ZK Z 5 Trlifajová K. Trlifajová K.
BIK-MLO Mathematical Logic 13KP+4KC Z,ZK Z 5 Klouda K. Klouda K.
BI-MLO Mathematical Logic 2P+1C Z,ZK Z 5 Trlifajová K. Starý J., Trlifajová K.
MI-MSI Mathematical Structures in Computer Science 2P+1C Z,ZK L 4 Starý J. Starý J.
MIE-MZI Mathematics for data science 2P+1C Z,ZK L 4
MI-MZI Mathematics for data science 2P+1C Z,ZK L 4 Starosta Š. Klouda K., Starosta Š., Vašata D.
MIE-MPI Mathematics for Informatics 3P+1R+1C Z,ZK Z 7 Starosta Š. Dolce F.
MI-MPI Mathematics for Informatics 3P+2C Z,ZK Z 7 Starosta Š. Starosta Š.
NI-MPI Mathematics for Informatics 3P+2C Z,ZK Z 7 Starosta Š.
MI-NNS Numeration systems in SageMath 1+1 Z Z 2
MI-EDW.16 Podnikové datové sklady 2P+1C Z,ZK L 5 Arnošt D. Arnošt D.
MI-PDM Practical Data Mining 2P+1C Z,ZK L 5 Kordík P. Friedjungová M., Klouda K., Kordík P., Vašata D.
BIE-PKM Preparatory Mathematics Z Z 4 Rybníčková J. Rybníčková J.
BIE-PST Probability and Statistics 2P+1R+1C Z,ZK Z 5 Novák P. Novák P.
BIK-PST Probability and Statistics 13KP+4KC Z,ZK Z 5 Novák P. Vašata D.
BI-PST Probability and Statistics 2P+1R+1C Z,ZK Z 5 Novák P. Novák P.
BI-VMM Selected Mathematical Methods 2P+2C Z,ZK L 4 Kalvoda T. Kalvoda T., Štampach F.
MI-VSM Selected statistical methods 4P+2C Z,ZK L 8 Hrabák P. Hrabák P., Vašata D.
NI-VSM Selected statistical Methods 4P+2C Z,ZK L 8
NI-PON Selected Topics in Optimization and Numerical mathematics 2P+1C Z,ZK L 5
MI-SCR Statistical Analysis of Time Series 2P+1C Z,ZK Z 4 Dedecius K. Dedecius K.
NI-SCR Statistical Analysis of Time Series 2P+1C Z,ZK Z 5
NI-LSM Statistical Modelling Lab 3C KZ L 5 Dedecius K.
MIE-SPI.16 Statistics for Informatics 4P+2C Z,ZK L 7 Hrabák P. Novák P.
MI-SPI.16 Statistics for Informatics 4P+2C Z,ZK L 7 Hrabák P. Hrabák P., Vašata D.
BI-SVZ Strojové vidění a zpracování obrazu 2P+2C Z,ZK Z 5 Jiřina M. Jiřina M., Novák J.
MI-TNN Theory of Neural Networks 1P+1C Z,ZK L 4 Holeňa M. Holeňa M.


Page updated 14. 12. 2019, semester: Z/2019-20, L/2018-9, L/2019-20, Z/2018-9, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška