Embedded Systems

These courses are the bare minimum of what you should know if you want to specialize in this field. Therefore, they are mandatory in this specialization. However, we also offer a number of other subjects relevant to your area of interest or suitably supplementing and expanding it. You can enroll in the optional courses.

• Advanced Digital Systems
• Data Coding and Compression
• Design of Embedded Systems
• Hardware/Software Codesign
• Practical Parallel Programming
• Real-Time Systems (in English)

If you are serious about your specialization, we recommend the following courses (of which there is otherwise a very wide range) from the optional ones. We think that they will be interesting for you either to deepen your knowledge of your specialization or to help you broaden your horizons in a suitable direction.

• Applied Evolutionary Algorithms
• Bio-Inspired Computers
• Bioinformatics
• Biometric Systems
• Computer Graphics
• Convolutional Neural Networks
• Cryptography
• Cyber-Physical Systems Design (in English)
• Data Communications, Computer Networks and Protocols
• Fault Tolerant Systems
• Graphic and Multimedia Processors
• High Performance Computations
• Image Processing
• Information System Security
• Intelligent Systems
• Multimedia
• Parallel Computations on GPU
• Principles and Design of IoT
• Projecting, Administration and Security
• Real-Time Systems (in English)
• Secure Hardware Devices
• Sensors and Measurement
• Simulation Tools and Techniques
• Speech Signal Processing
• Wireless and Mobile Networks

Whatever specialization you choose, you intend to become an engineer in the Information Technology and Artificial Intelligence degree programme. And this does not only mean knowing more, but it also means mastering a unique view of the world of informatics, which requires a certain perspective and foresight, the ability to solve problems beyond the capabilities of the so-called common sense. The set of compulsory courses for the entire programme not only offers a shift in the thinking of the engineer, but are above all a entry point for further expansion of knowledge in the essential directions of IT.

• Artificial Intelligence and Machine Learning is a course where you will learn how to teach computers to understand our world and force them to solve problems that are easy for humans, but difficult for an algorithmic machine to master.
• Computation Systems Architectures will teach you to think about how your code will run on modern computing platforms, how to think when programming so that you use resources as efficiently as possible, i.e. that your application makes the best use of modern platforms, uses system memory efficiently and is also efficient in terms of energy consumption.
• Data Storage and Preparation , especially large ones, and retrieving knowledge from them is an art useful for any computer scientist. This is one of the key aspects that strongly affects the effectiveness of many solutions and applications.
• Functional and Logic Programming will teach you that although classical imperative programming is a very widely used paradigm and very close to machine-level implementation, there are other approaches that will give you a new perspective on some key issues and help you get new and often more effective solutions.
• Modern Trends in Informatics (in English) (in English) is essential to see where the field is developing and what can be expected in practice in a few years.
• Parallel and Distributed Algorithms is a subject that will show you the laws, limits and pitfalls of parallel and distributed algorithmic solutions and associated synchronization mechanisms, without which you will hardly succeed in solving many more complex problems.
• Seminar of Discrete Mathematics and Logics
• Statistics and Probability are the right hand of every engineer, processing the numerical results of experiments or data obtained during the run of your application, analyzing them and learning from them for further decisions is almost his daily bread.
• Theoretical Computer Science shows the limits of the possibilities of computer science through formal languages and mathematical models of computation. This is the only way you can understand whether your problem is solvable at all and, if so, with what resources and with what means it can be proved.