Cursos

Sede donde se gestiona

Santander

Lugar de impartición

Santander - Península de la Magdalena

Dirección

Secretaría

ORGANIZADO EN COLABORACIÓN CON

Descripción de la actividad

AI has existed for 70 years and is far from being limited to generative AI. AI techniques such as multi-agent systems (MAS), particle swarm optimization (PSO), evolutionary algorithms (EA), feature extraction, and predictive analytics are highly attractive to industries for solving complex problems, optimizing processes, and making informed decisions. MAS enhances collaboration in supply chains and robotics, while PSO excels in logistics and scheduling. Evolutionary algorithms, inspired by natural evolution, allow for the development of innovative solutions in artificial life and data analysis. For signal pre-processing and feature extraction, AI is essential, especially in healthcare to improve patient care.

The objective of the summer school is to discover, understand, and use the different AI techniques currently in use: logic, agents, machine learning, deep learning, and generative AI.

The summer school provides a comprehensive platform for participants to explore these AI techniques through both theoretical presentations and practical applications, equipping them with the skills needed to address real-world challenges effectively.

The objective of this summer school is also to present AI approaches from different partners: researchers from 5 universities within the Eunice Alliance will present their work and teach how to design, build, produce, and use AI tools.

Another objective of this summer school is to facilitate exchanges between the participants and the researchers, to assist them in their personal work.

The summer school will feature online components preceding the on-site week. These online portions are designed to establish initial contacts and provide foundational knowledge to effectively approach the concepts studied during the in-person sessions.

During the on-site week of the summer school, participants will engage in practical exercises and demonstrations, which can be completed individually or in small groups. These hands-on activities are open to all and do not require prior completion of a computer science curriculum. The goal is to foster a collaborative learning environment where participants can apply theoretical knowledge to real-world scenarios, regardless of their academic background. The objective is that everyone can actively participate and benefit from the summer school experience.

Level of study: Mainly for PhD, but also Master’s Degree and last year of degree students doing their final thesis on the topic.