Passing the study plan
FI ZARD Processing and analysis of large-scale data
Name in Czech: Zpracování a analýza rozsáhlých dat
master's full-time specialized, language of instruction: Czech
Included in the programme: FI N-UIZD Artificial intelligence and data processing
master's full-time specialized, language of instruction: Czech
Included in the programme: FI N-UIZD Artificial intelligence and data processing
Study-related information
- Parts of the final state examination and its contentThe state final examination consists of two separately classified components: the thesis defence and the
the professional final examination. The entire state examination lasts approximately one hour (approximately 30 minutes for the defence, 30 minutes for the examination). The student has 15 minutes to present his/her thesis, and another 15 minutes are devoted to the analysis of the testimonials and discussion. In the subsequent oral final examination, the student answers unprepared questions, typically a debate of two to three questions, with at least one question from the common core of the follow-up study programme and at least one question from the student's chosen specialisation.
In order to pass the final examination, the student must be able to explain the basic concepts introduced in the programme profile courses, demonstrate the ability to apply the basic techniques, methods and concepts explained in the programme profile courses, and be able to respond to relevant supplementary questions or, where appropriate, develop the chosen topic in depth. If the student is unable to meet any of these requirements, the grade is unsatisfactory.
The submission of a thesis is a prerequisite for admission to the final examination. In the case of a negative evaluation of the thesis, the student may waive the defense, accept a failing grade, and proceed directly to the examination. In the case of an unsuccessful defence, it is not possible to withdraw from the examination.
Recommended progress through the study plan
Povinné předměty studijního programu
Code | Name | Type of Completion | Credits | Term | Profile Cat. |
FI:MA012 | Statistics II | zk | 3+2 | 1 | P |
FI:IV126 | Fundamentals of Artificial Intelligence | zk | 3+2 | 1 | Z |
FI:PA234 | Infrastuctural and Cloud Systems | zk | 3+2 | 2 | - |
FI:PA152 | Efficient Use of Database Systems | zk | 3+2 | 2 | Z |
FI:PV021 | Neural Networks | zk | 4+2 | 3 | Z |
FI:PV056 | Machine Learning and Data Mining | zk | 3+2 | 2 | Z |
FI:PV211 | Introduction to Information Retrieval | zk | 3+2 | 2 | Z |
FI:PV251 | Visualization | zk | 3+2 | 1 | Z |
FI:SOBHA | Defence of Thesis | SZk | - | 4 | - |
FI:SZMGR | State Exam (MSc degree) | SZk | - | 4 | - |
41 credits |
Master's thesis
Povinnost získat 20 kreditů z předmětu SDIPR.
Code | Name | Type of Completion | Credits | Term | Profile Cat. |
FI:SDIPR | Diploma Thesis | z | 20 | 4 | - |
20 credits |
Povinné předměty specializace
Code | Name | Type of Completion | Credits | Term | Profile Cat. |
FI:PA017 | Information Systems Management | zk | 2+2 | 1 | P |
FI:PA128 | Similarity Searching in Multimedia Data | zk | 2+2 | 2 | - |
FI:PA195 | NoSQL Databases | k | 3+1 | 2 | - |
FI:PA200 | Cloud Computing | k | 2+1 | 4 | P |
FI:PA212 | Advanced Search Techniques for Large Scale Data Analytics | zk | 2+2 | 2 | P |
FI:PA220 | Database systems for data analytics | zk | 2+2 | 1 | Z |
23 credits |
Datové algoritmy
Získat alespoň 4 kredity absolvováním předmětů z následujícího seznamu
Projekty a laboratoř
Získat alespoň 4 kredity absolvováním předmětů z následujícího seznamu