FI MU Study Catalogue 2024/2025
follow-up master's program (Czech) with specializations
The Artificial Intelligence and Data Processing program prepares students to work in the areas of design and development of intelligent systems and analysis of big data. These areas are currently undergoing very fast development and are becoming increasingly important. The program leads students to a thorough understanding of basic theoretical concepts and methods. During the study students also solve specific case studies to familiarize themselves with the currently used tools and technologies. Students will thus gain experience that will allow them to immediately use the current state of knowledge in practice, as well as solid foundations, which will enable them to continue to independently follow the developments in the field. The program is divided into four specializations that provide deeper knowledge in a chosen direction. Specializations share a common core, where students learn the most important mathematical, algorithmic, and technological aspects of the field. Machine Learning and Artificial Intelligence specialization lead graduates to gain in-depth knowledge of machine learning and artificial intelligence techniques and to gain experience with their practical application. Natural Language Processing specialization prepares graduates to work with natural languages (eg. Czech, English) in written and spoken form from the perspective of computer science. Data Management and Analysis specialization focus on data science, which creates value from big data by collecting, exploring, interpreting, and presenting data from different viewpoints with the goal of so-called business intelligence. Bioinformatics and Systems Biology specialization focuses on computational methods for automated analysis of large biological data and on creating predictive models of biological processes with the goal to better understand complex biological systems.
Due to the dynamic development of the area, the graduates have a wide range of career opportunities, with specific employment positions being created continuously during the course of their studies. Examples of different types of possible positions: positions in applied and basic research, typically concerning extensive data processing, often also in collaboration with experts from other disciplines such as biology or linguistics; positions in companies with an immediate interest in artificial intelligence and data processing (e.g., Seznam, Google) such as Data Scientist and Machine Learning Engineer; positions in companies that have extensive, valuable data (such as banking, telecom operators) or companies focusing on cloud data analysis, e.g., Business Intelligence Analyst or Data Analyst; graduates can also start their own start-up specializing in the use of artificial intelligence methods in a particular area.
Requirements for successful graduation
- Obtain at least 120 credits overall and pass the final state exam.
- Obtain 20 credits from SDIPR course and successfully defend Master's Thesis. See more details.
- Pass all the compulsory and elective courses of the program and selected specialization with the highest possible graduation form (unless explicitly stated otherwise).
- Fulfil requirements of at least one specialization.
Compulsory courses of the program
MA012
|
Statistics II |
---|---|
IV126
|
Fundamentals of Artificial Intelligence |
PA234
|
Infrastuctural and Cloud Systems |
PA152
|
Efficient Use of Database Systems |
PV021
|
Neural Networks |
PV056
|
Machine Learning and Data Mining |
PV211
|
Introduction to Information Retrieval |
PV251
|
Visualization |
SOBHA
|
Defence of Thesis |
SZMGR
|
State Exam (MSc degree) |
Specialization: Machine Learning and Artificial Intelligence
Machine Learning and Artificial Intelligence specialization leads graduates to gain in-depth knowledge of machine learning and artificial intelligence techniques and to gain experience with their practical application.
Compulsory courses of the specialization
IV111
|
Probability in Computer Science |
---|---|
IA008
|
Computational Logic |
PA163
|
Constraint programming |
Optimizations and Numeric Computing Pass at least 1 course of the following list | |
PV027
|
Optimization |
MA018
|
Numerical Methods |
PřF:M7PNM1
|
Advanced numerical methods I |
Applications of Machine Learning I Pass at least 2 courses of the following list | |
PA153
|
Natural Language Processing |
PA228
|
Machine Learning in Image Processing |
PA230
|
Reinforcement Learning |
Applications of Machine Learning II Pass at least 1 course of the following list | |
IA267
|
Scheduling |
PA212
|
Advanced Search Techniques for Large Scale Data Analytics |
PA128
|
Similarity Searching in Multimedia Data |
PV254
|
Recommender Systems |
PA164
|
Machine learning and natural language processing |
IA168
|
Algorithmic game theory |
Projects and Laboratory Obtain at least 4 credits by passing courses of the following list | |
PA026
|
Artificial Intelligence Project |
PV115
|
Laboratory of Knowledge Discovery |
IV127
|
Adaptive Learning Seminar |
IV125
|
Formela lab seminar |
PV253
|
Seminar of DISA Laboratory |
PV212
|
Seminar on Machine Learning, Information Retrieval, and Scientific Visualization |
Recommended course of study
Fall 2024 (1. term)
Spring 2025 (2. term)
-
PV056
Machine Learning and Data Mining -
PA152
Efficient Use of Database Systems -
PA234
Infrastuctural and Cloud Systems -
PV211
Introduction to Information Retrieval -
IA008
Computational Logic - Choice: Any course from Optimizations and Numeric Computing secion
- Choice: Any course from Applications of Machine Learning I secion
Fall 2025 (3. term)
- Choice: Any course from Optimizations and Numeric Computing secion
- Choice: Any course from Applications of Machine Learning I secion
- Choice: Any course from Applications of Machine Learning II secion
- Choice: Any course from Projects and Laboratory secion
-
SDIPR
Diploma Thesis
Specialization: Processing and Analysis of Large-scale Data
Processing and analysis of large-scale data specialization focuses on data science, which creates value from big data by collecting, exploring, interpreting, and presenting data from different viewpoints with the goal of so called business intelligence.
Compulsory courses of the specialization
PA017
|
Information Systems Management |
---|---|
PA128
|
Similarity Searching in Multimedia Data |
PA195
|
NoSQL Databases |
PA200
|
Cloud Computing |
PA212
|
Advanced Search Techniques for Large Scale Data Analytics |
PA220
|
Database systems for data analytics |
Data Algorithms Obtain at least 4 credits by passing courses of the following list | |
PA228
|
Machine Learning in Image Processing |
PV079
|
Applied Cryptography |
IA267
|
Scheduling |
PV254
|
Recommender Systems |
MA015
|
Graph Algorithms |
Projects and Laboratory Obtain at least 4 credits by passing courses of the following list | |
PV253
|
Seminar of DISA Laboratory |
PV115
|
Laboratory of Knowledge Discovery |
PV229
|
Multimedia Similarity Searching in Practice |
PA036
|
Database System Project |
Recommended course of study
Fall 2024 (1. term)
Spring 2025 (2. term)
Fall 2025 (3. term)
Specialization: Natural Language Processing
Natural Language Processing specialization prepares graduates to work with natural languages (eg. Czech, English) in written and spoken form from the perspective of computer science.
Compulsory courses of the specialization
IA161
|
Natural Language Processing in Practice |
---|---|
IV111
|
Probability in Computer Science |
PA153
|
Natural Language Processing |
PA154
|
Language Modeling |
IA008
|
Computational Logic |
Math Pass at least 2 courses of the following list | |
MA007
|
Mathematical Logic |
MA010
|
Graph Theory |
MA015
|
Graph Algorithms |
MV008
|
Algebra I |
MA018
|
Numerical Methods |
PřF:M7130
|
Computational geometry |
Natural Language Processing Pass at least 1 course of the following list | |
PA164
|
Machine learning and natural language processing |
PV061
|
Machine Translation |
IV029
|
Introduction to Transparent Intensional Logic |
Seminar or Project Obtain at least 2 credits by passing courses of the following list | |
PV173
|
Natural Language Processing Seminar |
PV277
|
Programming Applications for Social Robots |
PB106
|
Corpus Linguistic Project I |
PA107
|
Corpus Tools Project |
Recommended course of study
Fall 2024 (1. term)
Spring 2025 (2. term)
Fall 2025 (3. term)
Specialization: Bioinformatics and System Biology
Specialization Bioinformatics and System Biology is intended for students who want to acquire, besides the general knowledge of informatics, the latest knowledge in dynamically developing fields at the border of informatics and biology. By selecting this specialization, the student acquires deep knowledge about the processing, storage, and analysis of biological data or the use of formal methods for analysis and prediction of the behavior of biological systems.
Compulsory courses of the specialization
IV106
|
Bioinformatics seminar |
---|---|
IV108
|
Bioinformatics II |
IV110
|
Project in Sequence Analysis |
IV120
|
Continuous and Hybrid Systems |
PA054
|
Formal Methods in Systems Biology |
PA183
|
Project in Systems Biology |
PB050
|
Modelling and Prediction in Systems Biology |
PB172
|
Systems Biology Seminar |
PV225
|
Laboratory of Systems Biology |
PV290
|
Chemoinformatics |
Applications Pass at least 1 course of the following list | |
PV269
|
Advanced methods in bioinformatics |
PV270
|
Biocomputing |