Topics for Master degree state examination Open Informatics (accreditation 2016 and 2018)

Common part

  1. Polynomial algorithms for standard graph problems. Combinatorial and numerical theoretical algorithms, isomorphism, prime numbers. Search trees and their use. Text search based on finite automata. AE4M33PAL
  2. Problem/language complexity classes with respect to the time complexity of their solution and memory complexity including undecidable problems/languages.AE4M01TAL
  3. Combinatorial optimization problems including description of applications, problem formulation, complexity analysis and solving algorithms. AE4M35KO

Artificial Intelligence

  1. Learnability models: PAC and online. Learnability of conjunctions and disjunctions. Bayesian networks. Reinforcement learning. BE4M36SMU
  2. Resolution in the first order logic, automatic proving. Principles of automatic proving in Boolean domains and in predicate logic. Searching for models in generic domains. BE4M36LUP
  3. Minimizing empirical risk. Maximum likelihood estimation, EM algorithm. Deep networks and their learning. Classical and deep neural networks and their learning. BE4M33SSU
  4. Domain independent planning. Features, heuristics and algorithms. BE4M36PUI
  5. Autonomous agents and multiagent systems. Noncooperative game theory. BE4M36MAS
  6. Decision making, planning and coordination of autonomous systems of one or more robots. BE4M36UIR

Computer graphics

  1. Raster graphic. 3D objects and 3D scenes, transformations. Visibility, local illumination methods, shading and shadows. Radiometry, global illumination methods, texturing. B4M39APG
  2. Data structures for searching in multidimensional spaces. B4M39DPG
  3. Methods of 3D object representation and their animation. Tools to support the production process.B4M39MMA
  4. Basic datastructures for computational geometry, their representation and algorithms. B4M39VG
  5. Scientific visualization methods. Information visualization methods. B4M39VIZ
  6. Spatial geometry, image projection and perspective camera model for 3D reconstruction, virtual reality, visual odometry and SLAM. B4M33GVG

Human-Computer Interaction

  1. Scientific visualization methods. Information visualization methods. B4M39VIZ
  2. A formal description of user interfaces. Models of human behavior in relation to user interaction. Formal evaluation and prototyping. B4M39NUR
  3. User research and its role in HCI. Cognitive psychological concepts and their usage in HCI. B4M39PUR1
  4. Statistical analysis, models and their assessment. Dimensionality reduction. Clustering. B4M36SAN
  5. Principles of shape psychology. Essential composition and form principles. B4M39PTV
  6. The methodology of software testing. Methods for test creation from the application model. Automated testing.  B4M36ZKS

Software Engineering

  1. The methodology of software testing. Methods for test creation from the application model. Automated testing. B4M36ZKS
  2. Software architectures, their parameters and qualitative metrics. Architectural patterns, styles and standards. B4M36SWA
  3. Properties of parallel and distributed algorithms. Communication operations for parallel algorithms. Parallel algorithms for linear algebra.. B4M35PAG
  4. Effective algorithms and optimization methods. Data structures, synchronization and multithreaded programs. . B4M36ESW
  5. Big Data concept, basic principles of distributed data processing, types and properties of NoSQL databases. B4M36DS2
  6. Security analysis of operating systems, development of secure software and web applications security. Analysis of cyberattacks and malware. Security of mobile devices.  B4M36BSY

Computer Vision and Digital Image

  1. Basic datastructures for computational geometry, their representation and algorithms. B4M39VG
  2. Image representation for computer vision. Segmentation and image preprocessing methods. B4M33DZO
  3. Object detection in images. Image matching and correspondence search. B4M33MPV
  4. Spatial geometry, image projection and perspective camera model for 3D reconstruction, virtual reality, visual odometry and SLAM. B4M33GVG
  5. Algorithms for 3D geometric model reconstruction from images. A4M33TDV
  6. Minimizing empirical risk. Maximum likelihood estimation, EM algorithm. Deep networks and their learning. Classical and deep neural networks and their learning. BE4M33SSU

Data Science

  1. Statistical analysis, models and their assessment. Dimensionality reduction. Clustering. B4M36SAN
  2. Scientific visualization methods. Information visualization methods. B4M39VIZ
  3. Ontologies. Basic principles of ontological engineering, semantic web technologies, basic principles and technologies of linked data. B4M33OSW
  4. Minimizing empirical risk. Maximum likelihood estimation, EM algorithm. Deep networks and their learning. Classical and deep neural networks and their learning. BE4M33SSU
  5. Learnability models: PAC and online. Learnability of conjunctions and disjunctions. Bayesian networks. Reinforcement learning. BE4M36SMU
  6. Big Data concept, basic principles of distributed data processing, types and properties of NoSQL databases. B4M36DS2

Cyber Security

  1. Statistical analysis, models and their assessment. Dimensionality reduction. Clustering. B4M36SAN
  2. The methodology of software testing. Methods for test creation from the application model. Automated testing. B4M36ZKS
  3. Security analysis of operating systems, development of secure software and web applications security. Analysis of cyberattacks and malware. Security of mobile devices.   B4M36BSY
  4. Symmetric and asymmetric cryptography. Basic cryptosystems (RSA, El-Gamal). Number factorisation. Hashing algorithms. B4M01MKR
  5. Network routing principles. Network transport protocols. Software defined networks. Network function virtualization.. A0M32PST
  6. Principles of secure system design. Design and analysis of secure communication protocols, e.g. TLS, mobile telephony and others. Distributed system security.  B4M36KBE

Computer Engineering

  1. Design and implementation of in-chip integrated systems, application specific systems. B4M34ISC
  2. Advanced architectures of processors, memory and peripheral circuits and multiprocessor computers. B4M35PAP
  3. I/O and network interfaces of computer and embedded systems, hardware and software implementation.  B4M38KRP
  4. ARM based microcontrollers and signal processors; their functionality. Design and implementation of embedded systems for typical application areas. B4M38AVS
  5. Parallel and distributed algorithms. Communication within parallel algorithms. Parallel algorithms for linear algebra. B4M35PAG
  6. Efficient algorithms and their optimization. Data structures, synchronization and multi-thread programming. B4M36ESW

Bioinformatics

  1. Chemical composition of living matter, experimental models and methods, genetic code. B4M36MBG
  2. Modeling and analysis of biological sequences. B4M36BIN
  3. Image representation for computer vision. Segmentation and image preprocessing methods. B4M33DZO
  4. Statistical analysis, models and their assessment. Dimensionality reduction. Clustering. B4M36SAN
  5. Learnability models: PAC and online. Learnability of conjunctions and disjunctions. Bayesian networks. Reinforcement learning. BE4M36SMU
  6. Minimizing empirical risk. Maximum likelihood estimation, EM algorithm. Deep networks and their learning. Classical and deep neural networks and their learning. BE4M33SSU