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MASTER OF SCIENCE IN Intelligent Data Processing



Degree Conferred

Master of Science (MSc)

Double Diploma from KNRTU-KAI and  Kaiserslautern University of Technology

Entry Requirements

Bachelor Degree in IT

English level of A1/A2

Language of Instruction


Terms of Study

2 years (4 semesters)

1st and 2nd semesters – at KNRTU-KAI

3rd semester – at Kaiserslautern University of Technology, Germany

4th semester – at Kaiserslautern University of Technology, Germany or KNRTU-KAI

Study fee

Russian citizens (budget students) - free of charge

CIS countries students pay only part of the cost of German education (the Russian part of education is free) - 1,250 EUR per year

VISA countries students pay the full study fee - 2,500 EUR per year

Contact Persons

KNRTU-KAI, Russia - Dr. Sergey Zaydullin (SSZaydullin@kai.ru)


Curriculum (in English and Russian)

Module Handbook (in English and Russian)


  • Fundamentals of Image Processing: Fundamental processes of digital image processing. Image models. Spatial methods of image processing. Frequency-domain methods of image processing. Fundamentals of image segmentation. Methods of image segmentation.

  • Computer VisionBasic concepts and methodology of computer vision. Methods for determining the background and foreground objects in a video sequence. Image segmentation of the video sequence. Recognition of objects based on keypoints and contours. The recognition of object actions. Methods of three-dimensional reconstruction of the observed scene.

  • System of Pattern RecognitionBasic concepts of the pattern recognition theory. Design of pattern recognition systems. Supervised machine learning. Unsupervised machine learning. Fuzzy methods of pattern recognition. Neural network methods of pattern recognition.

  • Intelligent Information SystemsThe basic concept of Artificial Intelligence. Knowledge representation models. Knowledge reasoning. Machine Learning. Natural language processing. Natural language processing systems. Fuzzy sets. Fuzzy logic and fuzzy inference. Genetic algorithms. 

  • Artificial neural networks: Biological and artificial neurons. Unification of neurons in the network. Types of neural networks. Multifunctional neural networks - Perceptrons. Neural networks for clustering and data analysis - Self-learning neural networks. Neural networks for the classification of objects - Probabilistic neural networks. Neural networks for pattern recognition - Recurrent and Convoluted neural networks.