APM_53441_EP - Learning with tabular data (2025-2026)

This course offers a modern overview of statistical learning with tabular data, from classical tree-based models to emerging deep and foundation models. We will review the foundations of gradient boosting methods and their scalable implementations, cover recent deep learning models tailored for tabular data, and introduce tabular foundation models. In particular, we will discuss the limitations of LLMs on structured data, introduce the concept of in-context learning, and provide an in-depth understanding of novel tabular foundation models, including their architecture and pretraining strategies. The course will also address key practical challenges in real-world datasets and applications, such as encoding heterogeneous feature types (categorical, numerical, temporal, textual) and strategies for handling missing data.