Short Trainings & Workshops

Together with the rapid adoption of AI, regulatory expectations, public scrutiny, and the potential for unintended harms are rising. This workshop equips participants with the conceptual foundations and practical tools needed to adopt AI systems in an ethical and responsible manner.

We begin with an introduction to the ethics of AI in which we examine the concrete harms that AI systems can cause and the mechanisms through which these harms arise. Participants will also be introduced to current AI governance frameworks and learn how these can be operationalised to manage risks.

The second part of the day offers a deep dive into four core ethical principles — safety, privacy, fairness, and transparency. For each, we examine how risks relating to each principle materialise, how they can be assessed, and which technical and organisational mitigations are most effective.

Finally, an interactive session woven through the day follows an example use case across its full project lifecycle.  This exercise allows participants to apply governance practices, risk assessments, and mitigation strategies as they would within their own organisations.

The whole workshop can be tailored for a more technical audience (typically data scientists) or for decision makers without a technical data science background.

This workshop introduces a comprehensive framework for causal analysis, enabling participants to understand and model cause-effect relationships across domains, such as medicine (e.g., clinical trials), retail (e.g. customer engagement), economics (e.g., policy evaluation), and technology (e.g., recommendation systems).

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We begin by examining the limitations of predictive models and correlations, highlighting why they are often insufficient for answering “what if” questions or reasoning about interventions. To overcome these challenges and avoid common pitfalls, we present the foundations of causal reasoning, including counterfactuals, interventions, and causal graphs, and demonstrate how these concepts apply to real-world problems.

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The workshop is structured around two core areas: causal discovery and causal inference. Causal discovery focuses on uncovering underlying causal structures directly from data. We will explore a range of methods, with particular attention to their assumptions and limitations. Causal inference, in contrast, aims to estimate the effect of interventions or treatments on outcomes. We will cover both quasi-experimental approaches and modern machine learning techniques, along with practical strategies for evaluating their validity.

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Finally, two extended hands-on sessions will allow participants to apply these theoretical concepts in practice and quickly get started with causal machine learning using Python.

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.