An Introductory Course to Biomedical Informatics and Biomedical Information Retrieval

Biomedical Engineering

Biomedical technology deals with innovation in biomedical technologies, especially trends in the development of new devices or solutions that solve specific unmet needs. This course offers an introduction to the current trends on biomedical technologies and the innovation process to solve unmet clinical needs.

New Trends in Data Analytics

Extracting the value that can be hidden in data to support decision-making processes is hugely important today. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying, updating and information privacy. The course will review the different phases of the project to extract knowledge from databases with special emphasis on the extraction of predictive models.

Cybersecurity

Cybersecurity is about balancing risks and opportunities in cyberspace, the global interconnection of digital networks. Cybersecurity is designed to protect digital property, personal information, and business transactions from accidents, errors, and deliberate attacks. The course introduces students to the cybersecurity field, its uses and raison d’être.

Photovoltaic Solar Energy

This course offers a review of the fundamentals, technologies and applications of photovoltaic solar energy.

Towards the implementation of Hydrogen technologies

This course will provide insights about the state-of-the-art and development of the full value chain of the hydrogen economy from hydrogen generation to hydrogen off-takers. Insights about the different alternatives for hydrogen generation focusing on decarbonization, the storage and transportation and the potential off-takers and utilization of hydrogen will be provided. A general overview of the hydrogen value chain is the starting point for the understanding of the role of hydrogen in the transition to a decarbonised society, as well as the main challenges to be tackled for its implementation as energy vector and industrial commodity.

Cooperative, Connected and Autonomous Driving

The purpose of this course is to provide an overview of the state of the art and the key enabling technologies in the field of cooperative, connected and autonomous vehicles, such as positioning systems, environment perception and inter-vehicle communication. Students will analyse autonomous and connected driving applications and vehicle positioning requirements, be introduced to environment understanding technologies and autonomous vehicle decision-making algorithms, and compare vehicle communications technologies.

Drones

The civil applications of unmanned aerial vehicles (UAV) (also known as remotely piloted systems RPAS or commonly drones) are increasing. This short course mainly focuses on so-called multirotor drones. The two main challenges at present are endurance dependency on the electrical power supply (i.e. batteries) and autonomous maneuvering in relation to the dynamic, changing environment. This course provides a general overview of the different technologies involved in the execution of autonomous missions with drones, including the study of the flight system itself, estimation of positioning relative to the environment, and the control of these for maneuvering, as well as the definition and execution of complex high-level missions. Various industrial applications carried out by the Computer Vision and Aerial Robotics Research Group at the Universidad Politécnica de Madrid will also be presented, along with the current scientific and technological challenges faced by the international scientific community.

Introduction to Artificial Intelligence

Artificial Intelligence (AI) refers a kind of intelligence that enables machines to emulate intelligent behavior or to do things that people mostly do better. AI technologies have an important impact on our daily activities in business, as individuals, and as a society. During this course, apart from learning about AI fundamentals and applications, you will use specific techniques about different areas of AI such as knowledge representation and machine learning.

Deep Learning

Deep learning, a cutting-edge subset of artificial intelligence, is increasingly pivotal in driving technological and scientific advancements across various sectors. This comprehensive course on the fundamentals of deep learning is thoughtfully designed to guide students through the core concepts and applications of this transformative technology. It begins with an introductory module that lays a solid foundation in the basics of deep learning and artificial neural networks. The course then progresses into the practical realms of computer vision, demonstrating how deep learning techniques are revolutionizing image analysis and interpretation. In the natural language processing (NLP) module, students will explore the ways deep learning is used for understanding and generating human language, a critical aspect of AI’s interaction with people in products such as ChatGPT. The course also includes a focus on network analysis, where the course delves into graph neural networks and their role in predicting complex network dynamics. Each theoretical component is complemented by hands-on activities, ensuring a balanced approach that combines conceptual knowledge with practical skills, fostering both understanding and application in this rapidly evolving field.