This four-day school offers an overview of essential machine learning concepts and techniques, structured to build knowledge progressively.
Day 1: Foundations and Data Preparation The school kicks off by establishing a strong foundation. Participants will learn how to approach, visualize, and understand complex datasets using powerful dimensionality reduction techniques (like PCA and t-SNE), the critical steps of data preprocessing, focusing on cleaning, normalization, transformation, and strategies for handling missing data.
Day 2: Supervised Learning We dive into Supervised Learning, exploring how to train models using labeled data. The school covers the core methodology, including train/test/validation splitting and the essential metrics used to evaluate classification and regression models. This is followed by an exploration of classic ML algorithms.
Day 3: Unsupervised Learning The third day focuses on Unsupervised Learning, the science of finding hidden patterns in unlabeled data. Sessions will cover metrics for evaluating clustering quality (e.g., silhouette score) and introduce a range of fundamental clustering algorithms.
Day 4: Anomaly Detection The school concludes with a session on Anomaly Detection. This module will introduce some techniques used to identify outliers and unusual events in data.
The schedule also includes Flash Talk sessions offering participants an opportunity to present their own work and research to their peers.
The school can be followed remotely but we strongly suggest to participate in person in order to fully benefit of the presence of the tutors.
With only 100 in-person spots available, we'd greatly appreciate it if you could update your registration should you no longer be able to join us. This helps us ensure everyone who wants to attend has the opportunity to do so.
