19 Online Technology Summer Course Programs

This Summer School aim to provide an overview of the main Machine Learning and Deep Learning methods and their application to geophysical, geological, environmental and geographical data, keeping a more practical flavour.

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

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.

Getting things wrong is part of what makes us human, and our natural intelligence helps us learn from our mistakes. Reinforcement learning is an area of machine learning which enables artificial intelligence to learn from its mistakes as well, for example allowing a robot to use trial-and-error to interact with a new environment and achieve an objective. This advanced course examines the fundamentals of reinforcement learning and explores the varied applications of dynamic programming methods.

Deep Unsupervised Learning is an exciting emerging area of research in the field of artificial intelligence and machine learning, in which the goal is to develop systems that can learn from unlabelled data. Such systems closely mimic natural human intelligence by finding patterns in data without instructions on what to look for.

The Summer School intends to present some selected tools that can help humanists in their researches and activities, taking advantage of the digital turn and the new technologies. Particular attention is given this year to NLP, Digital Philology, GIS & AI.

The third edition of the Summer School “Microwaves and mm-waves for the Design of Advanced Wireless Links: Communication, Sensing and Power Transfer” aims to provide participants with a solid understanding of the fundamental principles and design strategies of modern wireless systems operating in the microwave and millimeter-wave frequency bands. It is co-coordinated by Prof. Paolo Nepa (Department of Information Engineering, University of Pisa) and Prof. Pietro Bolli, (Technology Director at the INAF Arcetri Astrophysical Observatory, Florence).

From self-driving cars and augmented reality to intelligent medical imaging helping doctors identify diseases more quickly, computer vision is a rapidly growing field within artificial intelligence and machine learning. In this course, students who are already familiar with the key theoretical foundations of artificial intelligence and machine learning will dive deeper into the exciting capabilities of this area of research and its applications.

The Summer School "Beyond 5G: Enabling Technologies, Opportunities and Research Challenges Ahead" is organized to offer 6 ECTS (48 hours of front lessons) in the broad field of 5G and beyond 5G (B5G) networks, targeting an audience of Bachelor (last year), Master and PhD students, early stage researchers and practitioners from Academia or Industry. The technologies, opportunities and research challenges of 5G networks will be presented and discussed during the school.

The 2026 edition of the Summer School Enabling Technologies for Industrial Internet of Things (ET-I2oT 2026) is organized to provide 6 ECTS (about 52 hours of front lessons) in the fields of Electronics, RFID, Sensors, Networking, Web of Things, SW for IoT, Computer Engineering, targeting on one side an audience of Bachelor (last year), Master and PhD students, and on the other side early stage researchers and practitioners from Academia or Industry. Both HW and SW aspects of IoT will be presented and discussed during the school.

Advanced Artificial Intelligence and Machine Learning: Natural Language Processing is a course that delves into the theoretical concepts of NLP, exploring its current applications and envisioning the potential future uses in diverse domains. The course highlights the significance of NLP in our daily lives, emphasizing its integration through predictive text, translation tools, and smart devices, showcasing the immense future potential within this exciting area of research, particularly evident in large language models like Chat-GPT.

This is the introductory prerequisite course in the computer graphics sequence which introduces students to the technical concepts behind creating synthetic computer-generated images. This is the first course in the computer graphics sequence at Stanford.

Archaeology deals with the study of the human past, conducted through material remains, i.e. artefacts that were manufactured, used, and discarded in ancient times. One of the most important task is to classify the artefacts, determining chronology, cultural attribution, form, function and other features. Neural networks and deep learning are powerful tools for supporting and facilitating such tasks, often time consuming and heavily depending on prior knowledge and expertise.

The course begins with an introduction to Computational Psychology and Artificial Intelligence, exploring the ways in which process-based computational models may be used to represent the workings of the human brain, employing algorithms to simulate aspects of cognition and predict behavior. We shall then turn to how such models correlate with neurobiology, the actual network of cells and signals which constitutes the brain, investigating neuron models, how neural networks perform computations, and neuropsychological theories of learning.

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