The Machine Learning and Artificial Intelligence section is dedicated to publishing research focused on the intersection of machine learning and artificial intelligence in the context of big data.

Led by Dr. Andrea Passerini from the University of Trento and Dr. Sebastian Ventura from the University of Cordoba, the Machine Learning and Artificial Intelligence section welcomes submissions in various domains of machine learning and artificial intelligence, which aim to enhance the understanding and application of these technologies in diverse fields.

Topics considered in the scope of this section include:

applied machine learning
automl and ai
classification, regression, recognition, and prediction
combinatorial optimization
constraint processing and learning
deep inference, learning, and architectures
explainable ml and ai
human-in-the-loop ai
learning, reasoning, and inference
learning to infer and to learn
learning to understand non-standard data
multi-agent inference and learning
problem solving and planning
program synthesis, probabilistic (logic) programming
reinforcement learning
robot learning
statistical relational ai
supervised and unsupervised machine learning
tractable inference and learning
trustworthy ai
visualization for and of ml

Submissions should provide detailed, in-depth knowledge about the intersection of machine learning and artificial intelligence in the context of big data.

The section welcomes submissions which support and advance the SDG: SDG 9: Industry, Innovation and Infrastructure.

The Machine Learning and Artificial Intelligence section does not consider submissions primarily focused on medical treatment, public health, or healthcare management, as these topics fall outside the scope of machine learning and artificial intelligence research. However, studies that emphasize the development and application of ML and AI techniques in the context of big data, including those that support and advance industry, innovation, and infrastructure, are welcome.

This multidisciplinary section is at the forefront of disseminating and communicating cutting-edge scientific knowledge and impactful discoveries in the field of machine learning and artificial intelligence to researchers, industry, policymakers, and the public worldwide.

Robots that Learn and Reason: Towards Learning Logic Rules from Noisy Data