Short Trainings & Workshops

This hands-on workshop provides a deep dive into advanced AI techniques, focusing on structured outputs from Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Multimodal AI. Participants will engage in practical exercises to refine LLM outputs, apply advanced RAG methods to medical texts, and explore agentic AI concepts using Hugging Face libraries. Ideal for those looking to enhance their AI workflows with cutting-edge methodologies.

Bias mitigation techniques can be used to ensure fairness and impartiality in the outcomes of machine learning models, using robust data collection, preprocessing, in-processing, and post-processing methods. In this seminar, we will present the most popular techniques, and we will show some examples of their application.

This workshop provides an overview of techniques to model and predict propensity, namely the probability that a person will perform a given action in the future, based on past observations. It includes time-window classification and survival analysis, as well as customer lifetime value and causal analysis.

This workshop provides a general introduction to computer vision. It covers typical tasks where computer vision algorithms are employed, elaborationg on the basic principles and how to implement solutions (libraries, data prep, best practices,…). Such tasks include supervised and non-supervised learning (image recognition, object detection), transfer learning and diffusion models. At the end of the course we present a concrete use case from a company that uses computer vision to map & track marine plastic.

This workshop provides a high level overview of MLOps, focusing on the deployment of proof-of-concept AI solutions. We will introduce basic MLOps concepts and best practices, and present business use cases and corresponding specific recommendations.

This course offers an introduction to the recent developments of the Large Language Models. After a high level introduction about transformers, the course focus on LLMs and its applications. A special attention is given to ChatGPT and to the RLHF technique. The second part of the course is dedicated to a hands-on session (in Renku or Google Collaboratory) with examples and exercises on how to use models and libraries in Huggingface.

In this seminar the participants will learn what time series data is and its real-world applications. We will review time series key concepts, decomposition and the most popular forecasting methods. We will also explore clustering methods specifically designed for time series and we will conclude with some real world applications of time series analysis.

Explainability of AI models has become an important tool to deploy effectively many AI solutions, and to increase trust from the stakeholders of these solutions. In this workshop, we will introduce the most popular methods for explaining AI predictions, with a few examples of real use cases. Moreover, we will have a hands-on session with an exercise to try these techniques on a sandbox dataset.