INAF SCHOOL OF AI

14 Apr 2026, 09:00 → 17 Apr 2026, 15:00 Europe/Rome

Auditorium Nazionale Ernesto Capocci (INAF Astronomical Observatory of Capodimonte (Naples))

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.

 

This is the second in a series of events organized and supported by the AI Thematic Group of the INAF USC-C Computing Division. The primary goal of the thematic group is to facilitate the sharing of knowledge, experiences, and best practices, as well as to provide support and promote collaboration among INAF researchers who are currently using or planning to use machine learning techniques in their research. Further information about the group, including its scope, contacts, and how to join, is available here: 

 

https://usc8.inaf.it/tematic-groups/ai-in-astronomy/

Tuesday 14 April

14:00 → 14:30 Welcome and Logistics

14:30 → 16:00 Introduction

Participants will learn how to approach, visualize, and understand complex datasets using powerful dimensionality reduction techniques (like PCA and t-SNE). The afternoon is dedicated to the critical steps of data preprocessing, focusing on cleaning, normalization, transformation, and strategies for handling missing data (imputation).

16:00 → 16:30 Coffee Break

16:30 → 18:00 Introduction

Participants will learn how to approach, visualize, and understand complex datasets using powerful dimensionality reduction techniques (like PCA and t-SNE). The afternoon is dedicated to the critical steps of data preprocessing, focusing on cleaning, normalization, transformation, and strategies for handling missing data (imputation).

18:00 → 19:00 Flash Talks

Wednesday 15 April

09:30 → 11:00 Supervised Learning

We dive into Supervised Learning, exploring how to train models using labeled data. The session 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.

11:00 → 11:30 Coffee Break

11:30 → 13:00 Supervised Learning

We dive into Supervised Learning, exploring how to train models using labeled data. The session 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.

13:00 → 14:15 Lunch & Group Photo

14:15 → 15:30 Supervised Learning

We dive into Supervised Learning, exploring how to train models using labeled data. The session 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.

15:30 → 16:00 Coffee Break

16:00 → 17:30 Supervised Learning

We dive into Supervised Learning, exploring how to train models using labeled data. The session 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.

17:30 → 18:30 Flash Talks

Thursday 16 April

09:30 → 11:00 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.

11:00 → 11:30 Coffee Break

11:30 → 13:00 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.

13:00 → 14:00 Lunch

14:00 → 15:30 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.

15:30 → 16:00 Coffee Break

16:00 → 17:00 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.

18:30 → 22:30 Walking tour of the hystorical center

Friday 17 April

09:30 → 11:00 Anomaly Detection

This module will introduce robust techniques used to identify outliers and unusual events in data.

11:00 → 11:30 Coffee Break

11:30 → 13:00 Anomaly Detection

This module will introduce robust techniques used to identify outliers and unusual events in data.

13:00 → 14:00 Lunch

14:00 → 15:00 Closing Remarks and Round Table