This summer school aims to provide an overview of the main machine learning methods and their application to geophysical, geological, and environmental data, keeping a more practical flavor.

The Summer Course in Statistical and Econometric Analysis of Network Data provides students with the knowledge and skills to analyze the role of networks in diverse social and economic environments. Participants will delve into recent econometric methods specifically designed for analyzing network data, recognizing the growing significance of networks in social, business, and economic contexts. The course acknowledges the increasing importance and accessibility of network data, attributed to the surge in online social media usage and digitization.

These new Quantum-based systems will not replace Classical Information and Communications ones, but will be run in conjunction with them by enhancing them where Classical ones present critical weaknesses, such as the impossibility of simulating some biochemistry systems in the pharmaceutical systems, achieving nano-scale sense of physical magnitudes, solving some hard computing problems, or warranting secure communications that cannot be never deciphered by third parties.

In our age of burgeoning smart technology and automation, we are already seeing the transformative potential of Artificial Intelligence and Machine Learning in fields as diverse as finance, medicine, and manufacturing. This course offers a hands-on introduction to this future-focused area of research.

The purpose of the summer school is to deepen the participant's knowledge and skills in defense (Cyber) and 5G by using Agile (Scrum framework) methodology. Lectures held by the leading specialists will be combined with practical assignments and company visits.

Introduction to circuit modeling and analysis. Topics include creating the models of typical components in electronic circuits and simplifying non-linear models for restricted ranges of operation (small signal model); and using network theory to solve linear and non-linear circuits under static and dynamic operations.

Cyber Security is an ever-evolving field with opportunities waiting for everyone with the interest to learn more and develop their skills in this interesting and demanding topic. This module will show the basic steps most hackers would go through while compromising the victim’s computers and it will define some interesting known and less-known attack vectors and protections against them.

This module presents a vital core for all hardcore programmers, with very strong prerequisites and excellent knowledge of Java and C# programming languages. Therefore, upon successful completion of the module, the students will, with no exception, witness their own quantum leap in knowledge, learning both the Kotlin programming language and Android mobile architecture.

Artificial Intelligence is a historical field of computer science that has been completely revolutionized by the emergence of deep learning in the last decade. This course also explores the interplay between cybersecurity and smart cities, focusing on how digital infrastructure and IoT (Internet of Things) technologies can be secured in urban environments.

This course will promote students' good understanding of the Big Data eco-system, and current trends, highlight the Big Data challenges, allow students to build on-demand Big Data applications and show them how to solve advanced analytics problems with Big Data using cutting-edge technologies.

The goal of this course is to provide you with a quick but solid introduction to a theoretical foundations of machine learning.

Deep Tech startups, known for their breakthroughs in science and engineering, are attracting significant interest from investors and policymakers. The 2023 European Deep Tech Report highlights this sector as one of the most resilient in venture capital, with $15 billion invested in 2023, positioning Europe as a potential leader in Deep Tech.

The 6th International Summer School on Resource-Aware Machine Learning (REAML 2022) will be held this year from Sept. 12-16, 2022. The school provides lectures on the latest research in machine learning, typically with a twist on resource consumption and how these can be reduced.

Machine Learning & Applied Statistics Summer School will introduce you to a range of quantitative methods from mathematics, statistics and computing and will enable you to use these methods in applications in various fields including finance and bioimaging.