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A group consists of courses that have the same role in a study plan. A group thus facilitates the requirement on credits to be acquired in a prescribed structure for a study plan. Hence, a student just cannot accumulate the required amount of credits required by the study plan; s/he must meet the requirements of each group of courses of a given study plan.
       Each student has to complete either at least a prescribed minimum (amount of) credits or to successfully complete a prescribed minimum (amount of) courses for a given group.
If a group has defined a minimum amount of credits = total amount of credits that can be obtained from the group, the student must successfully complete all the courses of the group. Such a group of courses has its role marked as 'compulsory'. If a group has defined a minimum amount of credits < total amount of credits obtainable from the group, such a situation is referred to as a group with an obligation to choose and complete at least the minimally set amount of credits. Similarly for courses.
If a group has defined a minimum amount of credits = 0 and a minimum number of courses = 0 at the same time, then the courses in the given group are elective.
Ifa a group has defined a minimum amount of credits < maximum amount of credits < total amount of credits obtainable from the group, then credits earned earned above the minimum amount of credits are seen as elective and credits above the maximum amount of credits from a given group do not count.
For ease of reference, each group has a role of the courses in the given study plan assigned next to its name.
The list is sorted alphabetically by the Department code and Course title.
Group: Profiling Courses of Master Specialization Knowledge Engineering, v. 2020, in Czech
Min. credits: 0   Credits total: 35   Min. courses: 0 Role: VO - Compulsory Elective Modules for Branches and Specializations
Course Course title Extend of
teaching
Comple-
tion
Seme-
ster
Recomm.
sem.
Cre-
dits
Instruc-
tor
Cat.
NI-UMI Artificial intelligence 2P+1C Z,ZK Z 1 5 Surynek P. 18105
NI-MVI Computational Intelligence Methods 2P+1C Z,ZK Z 1 5 Kordík P. 18105
NI-PDD Data Preprocessing 2P+1C Z,ZK Z 1 5 Jiřina M. 18105
NI-BML Bayesian Methods for Machine Learning 2P+1C KZ L 2 5 Dedecius K. 18105
NI-ADM Data Mining Algorithms 2P+1C Z,ZK L 2 5 Kordík P., Vašata D. 18105
NI-PON Selected Topics in Optimization and Numerical mathematics 2P+1C Z,ZK L 2 5 Klouda K., Starosta Š. 18105
NI-SCR Statistical Analysis of Time Series 2P+1C Z,ZK Z 3 5 Dedecius K. 18105

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Page updated 21. 4. 2020, semester: L/2020-1, Z,L/2019-20, Z/2020-1, Send comments to the content presented here toAdministrator of study plans Design and implementation: J. Novák, I. Halaška