The goal is to familiarize students with signal analysis and their transfer through linear systems, types of modulation techniques and the effects of noise over modulation techniques. 

The aim of the course is to provide introductory knowledge on selected topics in the field of knowledge systems. It introduces the basic concepts and architecture of expert systems, knowledge acquisition and knowledge representation, and various aspects related to decision support. Students will learn and acquire a deeper understanding of expert systems and with the provided assignments they will be trained to developed practical skills for designing expert systems. 

To provide students in computer sciences with an basic knowledge of vectors and matrices and their application in the field of informatics. To learn the concepts and methods of linear algebra and how they can be applied in solving computational problems that arise in computer science. After passing the exam of this course the students should be able to perform standard operations on matrices, to solve and give interpretation of the solution of the system of linear equations, describe the main properties of finitely dimensional vector spaces and linear transformation and apply the method of linear algebra for modeling and solving problems in computer sciences.

The goal of this course is to learn the basic concepts of logical and functional programming. Students will develop programs using declarative and functional programming. Topics that will be covered in this course are good basis for the incoming problems from the domain of artificial intelligence / intelligent systems. 

The goal of the course is to introduce the students to the basics of the modern machine learning techniques. After completion of the course the students will: have deeper knowledge of advanced techniques and methods of machine learning; be able to apply successfully the machine learning algorithms for solving real world problems; be able to conceptualize, analyze, realize and estimate the performances of a machine learning system.