The industrial relevance of advanced AI/ML methods

The goal of this session is to give participants a better insight into the type of problems that can be tackled using machine-learning techniques, illustrated by several real-world examples from a diverse set of domains.

Among others, the following machine-learning methodologies will be covered:

• Reinforcement learning
• Deep learning
• Bio-inspired computing
• Active learning
• Federated learning

For each method, its characteristics, advantages and disadvantages will be explained in more detail, as well as the most commonly used algorithm(s) to solve a particular industrial problem. The aim is to guide the participants in making a conscious choice for the appropriate technique in function of the problem setting at hand, as well as the available data (dimensionality, attribute types, etc.) and the expected model requirements (interpretability, accuracy, scalability, etc.).