Programme

The Brno Summer School in Information Technology offers courses in three important areas of information technology:

  1. Cyber Security and Forensics
  2. Machine Learning
  3. Interactive Applications
  4. Introduction to the Cloud
  5. Robot Programming

Students choose any of these courses. The courses cannot be combined.

Enroll

A typical day

The timetable shown below offers an idea of a typical daily schedule, although this may vary depending on the events planned for the given day.

  • 09:00-12:00 Morning academic session
  • 12:00-13:00 Lunch
  • 13:00-16:00 Afternoon academic session

CYBER SECURITY AND FORENSICS

The course introduces students to the basics of filesystems, operating systems, and their use in the area of digital forensics. They will learn essential cybersecurity principles and incident response techniques. Moreover, the attendees will try out various attacks, their detection, and how to create countermeasures.

Key outcomes

  • Understanding fundamental concepts of cybersecurity and digital investigation
  • Learning how to acquire a forensic image and examine filesystems, OS, and data
  • Gaining skills for incident responses and forensics with real-world cybersecurity case studies
  • Learning the essentials of cryptography, password protection, and cracking
  • Getting hands-on experience to develop skills via open-source security tools
  • Try out real cyber attacks in a lab environment and learn how to protect your system against such attacks

Lecturers

Ondřej Ryšavý, Radek Hranický, Jan Pluskal, Patrik Holop, Anton Firc, Petr Hanáček and Kamil Malinka

Ondřej Ryšavý, doc. Ph.D.

Ondřej Ryšavý is a leader of the NES@FIT research group that specializes in cybersecurity and digital forensics. He has lead numerous R&D projects and conducted collaboration with industrial partners in the area of system and network security, in particular, security network monitoring, threat detection, and industrial system cybersecurity.

MACHINE LEARNING

Students will get a broad perspective on the latest topics in machine learning and they will gain solid practical foundations to be able to solve advanced problems on their own. They will learn general data processing and machine learning methods, focusing on practical implementation in Python. The students will also learn general concepts of deep learning, including convolutional and recurrent networks. Lectures will also focus on specific state-of-the-art approaches in object and scene understanding from images, speech recognition and speaker identification, as well as language modelling and understanding

Key outcomes

  • Gaining practical understanding of basic concepts of machine learning and data processing
  • Being able to use convolutional and recurrent networks in practical applications
  • Understanding basic concepts of computer vision
  • Understanding the structure and parts of a speech recognition pipeline
  • Understanding how words are represented in natural language processing methods
  • Being able to extract semantic information, from text
  • Experiencing how practical problems can be solved by machine learning in a team project

Lecturers

Lukáš Burget, Michal Hradis, Lukáš Sekanina, Martin Fajčík and Karel Veselý

Lukas Burget, doc. Ph.D.

Lukas Burget, an assistant professor at FIT BUT and the research director of the BUT Speech@FIT group, is interested in speech data mining, concentrating on acoustic modelling for speech, speaker and language recognition, including the respective software implementations. Besides his achievements in EU- and US- funded projects (US-Air Force EOARD, IARPA BEST etc.), he is a distinguished lecturer in classification and recognition courses.

INTERACTIVE APPLICATIONS

The students will learn about the importance of user interfaces for efficient computer usage. They will be acquainted with basic principles and structure of applications and user interface development tools, master various aspects of the UI design process: from design thinking and user-centred design, to architecture design of web applications. Participants will practice their skills with modern development tools and technologies, design and develop a functional web application, and develop their presentation and teamwork skills.

Key outcomes

  • Understanding of the bigger picture by studying the past, present, and future of user interfaces and human-machine interaction
  • Understanding the design process focused on human-centred design
  • Critically analysing contemporary websites, learning how they are built and deployed
  • Understanding information system architecture
  • Learning to design and develop front-end web applications based on classic and modern technologies
  • Completing a team project that builds on multiple syllabus criteria

Lecturers

Adam Herout, Víťa Beran , Radek Burget, Libor Polčák, Jarek Dytrych and Jan Pluskal

Adam Herout, prof. Ph.D.

Prof. Adam Herout leads the Graph@FIT research group. His research interests are centred around computer vision, especially traffic surveillance. He is active in the start-up world and co-founded a few start-ups by himself. That led him to his interest in customers and users, in user experience, and in usability of IT services. Besides his education in information technology, specifically in computer graphics and computer vision, he took courses in group psychotherapy and Gestalt coaching.

INTRODUCTION TO THE CLOUD

This is an introductory course to cloud computation. Theory covering vendor-specific services and practical demonstrations will occur on the Google Cloud Platform. Students will learn basic principles and techniques, allowing them to move from on-premises to the Cloud as effortlessly and painlessly as possible. Additional emphasis is given to native cloud development that opens a door for serverless applications to surpass the old and cumbersome monoliths. After the course completion, students will be confident to make business decisions on cloud migration and draw upon their experience with cloud-native technologies qualifying them to analyze Big Data and apply Machine Learning.

Key outcomes

  • Understanding of Cloud Basic concepts and why it is a technology and business game changer
  • Learning how to Compute on Cloud
  • Being able to implement a variety of structured and unstructured Storage models
  • Understand the different application-managed service options in the cloud
  • Being familiar with security in the cloud
  • Use and design secure networks spanning multiple datacenters
  • Being capable of identifying cloud automation and management tools
  • Discover a variety of managed Big Data services in the cloud
  • Explain what Machine Learning is, the terminology used, and its value proposition

Lecturers

Jan Pluskal, Kamil Jeřábek

Jan Pluskal, Ing.

Jan Pluskal is a member of the NES@FIT research group. His area of expertise is full-stack .NET platform development with DevOps and Cloud overlap. He usually works as an architect and team lead on security research projects. Beyond the university occupation, he is a freelancer, professional lecturer, and ex-Google Authorized Trainer.

ROBOT PROGRAMMING

Students will learn about robotics, from low-level sensors to high-level planning and controlling. They will learn about programming of Arduino and robotic sensors, connecting various components to Arduino, and interfacing Arduino to a higher-level computer. Lectures will also focus on basic robotics algorithms like localisation, map making, and path planning. Attention will also be paid especially to the internationally recognised robotic framework (ROS) for utilizing studied algorithms. The students will also have an opportunity to learn about agents and multiagent systems for highest-level planning and decision-making.

Key outcomes

  • Learning about sensors used in robotics
  • Connecting components to Arduino and programming I/O operations
  • Interfacing Arduino to a PC with ROS
  • Learning robotics algorithm for path planning, localization, and SLAM
  • Working with ROS
  • Learning about high-level planning and decision making for agents

Lecturers

Jaroslav Rozman, Daniel Bambušek, and Jan Beran

Jaroslav Rozman, Ph.D.

Jaroslav Rozman is an assistant professor at FIT BUT. His research interests are in robotics, artificial intelligence and computer vision domain, particularly Mobile Robot Navigation. He is teaching courses about Robotics and Fundamentals of Artificial Intelligence. In the European project for autonomous warehouses, he designed a system for autonomous forklifts. He took part in an expert group preparing document about autonomous cars for Czech Ministry of Transport. His big hobby is also genealogy, and he is now leader of genealogical project funded by national Technology Agency of the Czech Republic.

Opening and Graduation ceremony

The opening ceremony takes place the very first day of the course. A common inaugural dinner will be held so that the students have an opportunity to get to know each other better.

The final evening of the programme celebrates the achievements of each participant in a graduation ceremony followed by a gala dinner.

Graduation

Students who complete at least 80% of the academic programme and finish course tasks or a final project of sufficient quality (50/100 at least) will receive a certificate of completion of the course worth 5 ECTS credits.

Excursions

The programme includes visits to two IT companies selected according to the main topics of the courses. The students will also be offered an excursion to the JIC (South Moravian Innovation Centre), which empowers entrepreneurs and businesses in all stages of development.