USC-C General Assembly - Trieste

:... Machine Learning

Machine Learning for Astrophysicists: An Introduction

This introductory lecture aims to provide astrophysicists with a foundational understanding of Machine Learning (ML) concepts, tools, and applications relevant to modern astronomical research. In the era of increasingly large and complex datasets from surveys and simulations, ML techniques are becoming indispensable for discovery and analysis.

We are going to cover the core paradigms of Machine Learning, focusing on Supervised Learning (e.g., classification of transient events and photometric redshift estimation) and Unsupervised Learning (e.g., clustering of galaxies and anomaly detection in time series data). The session introduces some algorithms, highlighting their main characteristics and typical applications. It also explores the general workflow of a machine learning project, including data collection, model training, evaluation, and deployment.
The goal is to equip astrophysicists with the conceptual tools needed to critically assess and effectively apply machine learning methods in their own research.

When: Wednesday 11/03/2026 from 4:30 pm to 6:30 pm

Where: Savoia Excelsior Palace - Sala Tergeste

Streaming Link: meet.google.com/xen-gyvu-unv

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