Training and deploying Large-Scale Models
This course introduces the foundations and practices of training modern Large Language Models (LLMs) at scale. Students will learn how deep learning models are trained across multiple GPUs, nodes, and clusters, and why distributed training is the key to enabling
Stochastic calculus in machine learning : sampling and generative modeling
Stochastic calculus has become an essential tool for understanding and justifying modern machine learning techniques, such as the Unadjusted Langevin Algorithm and diffusion models. To equip students with the necessary background, this course offers an introduction to stochastic calculus and
Multimodal Explainable AI (XAI)
Course Summary: This course explores Explainable Artificial Intelligence (XAI), a crucial subfield of machine learning dedicated to enhancing the transparency of complex models. While modern AI systems—particularly Deep Neural Networks (DNNs) and Foundation Models achieve state-of-the-art performance, their black-box nature
Artificial Intelligence and Computer Vision for Cultural Heritage
Art history and cultural heritage science are undergoing rapid advances due to the simultaneous advent of new and inexpensive imaging modalities, fast computation, and ubiquitous high-speed internet. We can easily collect gigabytes or terabytes of multimodal imaging data about an
Representation Learning for Computer Vision and Medical Imaging
Good and expressive data representations can improve the accuracy of machine learning problems and ease interpretability and transfer. For computer vision and medical imaging tasks, handcrafting good data representations, a.k.a. feature engineering, was traditionally hard. Deep Learning has changed this
LLM for code and proof
Recent advances in large language models (LLMs) have enabled remarkable progress in program synthesis and code generation. This course explores the foundations and methodologies behind modern neural code generation, with a particular focus on Transformer-based architectures and LLM techniques. The
Geometry Processing and Geometric Deep Learning
This course will introduce students to advanced topics in modern geometric data analysis (the field known as Geometry Processing) with focus on: mathematical foundations (discrete differential geometry, mapping, optimization), and deep learning for best performing We will give an overview
Interactions
Interactions are an integral part of many systems, be they natural, social, artificial and/or virtual. Their modelling and analysis has long posed challenges to the sciences – one may think of the three-body problem in classical mechanics, chaos theory in
Algorithms and learning for protein science
Proteins underlie all biological functions, yet, understanding their mechanisms at the atomic scale remains a fundamental open problem. The difficulties are inherent to complex dynamics in very high dimensional spaces. Indeed, with circa 5000 atoms and xyz coordinates per atom,
Statistical learning with extreme values
With the ubiquity of sensors, Big Data are now increasingly available in a wide variety of domains of human activity (science, industry, health, environment, commerce, security, …) and rare/extreme phenomena are becoming observable in a significant manner. Before, such events
