FI MU Study Catalogue 2019/2020
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 subject 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.
- Fulfil requirements of at least one specialization.
Compulsory subjects of the program
MA012
|
Statistics II |
---|---|
IV126
|
Artificial Intelligence II |
PA039
|
Supercomputer Architecture and Intensive Computations |
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: 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 subjects of the specialization
IV106
|
Bioinformatics seminar |
---|---|
IV108
|
Bionformatics II |
IV110
|
Bionformatics project I |
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 |
PV027
|
Optimization |
PV225
|
Laboratory of Systems Biology |
Applications Pass at least 1 course of the following list | |
PV269
|
Advanced methods in bioinformatics |
PV270
|
Biocomputing |
Recommended course of study
Fall 2019 (1. term)
Spring 2020 (2. term)
Fall 2020 (3. term)
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 subjects of the specialization
IV111
|
Probability in Computer Science |
---|---|
PA153
|
Natural Language Processing |
Logic and Algorithms Pass at least 3 courses of the following list | |
MA007
|
Mathematical Logic |
PA164
|
Machine learning and natural language processing |
IA008
|
Computational Logic |
PA163
|
Constraint programming |
IA168
|
Algorithmic game theory |
Applications of Machine Learning Pass at least 1 course of the following list | |
PA167
|
Scheduling |
PA212
|
Advanced Search Techniques for Large Scale Data Analytics |
PA128
|
Similarity Searching in Multimedia Data |
PV254
|
Recommender Systems |
Projects and Laboratory Obtain at least 4 credits by passing subjects of the following list | |
PA026
|
Artificial Intelligence Project |
PV115
|
Laboratory of Knowledge Discovery |
IV127
|
Adaptive Learning Seminar |
IV125
|
Formela lab seminar |
Optimizations and Numeric Computing Pass at least 1 course of the following list | |
PV027
|
Optimization |
MA018
|
Numerical Methods |
PřF:M7PNM1
|
Advanced numerical methods I |
Recommended course of study
Fall 2019 (1. term)
Spring 2020 (2. term)
Fall 2020 (3. term)
Specialization: Data Management and Analysis
Data Management and Analysis 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 subjects of the specialization
PA017
|
Software Engineering II |
---|---|
PA195
|
NoSQL Databases |
PA200
|
Cloud Computing |
PA212
|
Advanced Search Techniques for Large Scale Data Analytics |
PA220
|
Database systems for data analytics |
PV062
|
File Structures |
PV065
|
UNIX -- Programming and System Management I |
PV079
|
Applied Cryptography |
Projects and Laboratory Obtain at least 4 credits by passing subjects of the following list | |
PV253
|
Seminar of DISA Laboratory |
PV115
|
Laboratory of Knowledge Discovery |
Recommended course of study
Fall 2019 (1. term)
Spring 2020 (2. term)
Fall 2020 (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 subjects of the specialization
IA161
|
Advanced Techniques of Natural Language Processing |
---|---|
IV111
|
Probability in Computer Science |
PA153
|
Natural Language Processing |
PA154
|
Language Modeling |
PA156
|
Dialogue Systems |
Math Pass at least 2 courses of the following list | |
MA007
|
Mathematical Logic |
IA008
|
Computational 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
|
Introduction to Machine Translation |
IV029
|
Introduction to Transparent Intensional Logic |
Seminar or Project Obtain at least 2 credits by passing subjects of the following list | |
PV173
|
Natural Language Processing Seminar |
PB106
|
Corpus Linguistic Project I |
PA107
|
Corpus Tools Project |