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Introduction to Machine Learning in Geosciences


Please do not pay the tuition fees before receiving the admission letter


A large number of applications that only a few years ago would have been considered impossible to be performed without any sort of human interaction are now autonomously executed by increasingly more powerful machines and sophisticated algorithms. Fed by an enormous quantity of available data, machine learning algorithms can learn, without being explicitly programmed, to solve complex tasks such as speech, face, and object recognition or to play and even defeat the best human players at the ancient game of Go.

Machine-learning is becoming an essential skill in many data-intensive scientific fields, including Earth Sciences related disciplines. 

In many fields of Geosciences datasets are growing in size and variety at an exceptionally fast rate, highlighting the need for new data processing and assimilation techniques that are able to exploit the information deriving from this data explosion. Machine-learning techniques have the potential to push forward the state of the art of data analysis procedures used in different fields of the Geosciences. In this context, we propose a summer school that focuses on the use of Machine Learning techniques to geophysical, geological and environmental data.

The school will cover topics listed below. Each topic will be accompanied by specific practical sessions, focused on the solution of general geophysical, geological and environmental problems.

Overview of the course and general machine learning concepts

Supervised Learning
Regression (Linear and Non-linear regression techniques)
Classification (Logistic Regression, K-NearestNeighbors and Support Vector Machines)

Unsupervised Learning
Clustering (k-means, Hierarchical Clustering, DB-Scan)
Data Reduction (PCA and ICA)

Deep Learning
Basics on Artificial Neural Networks (Activation function, Back-propagation, Training and Optimization)
The Multi-Layer Perceptron
Convolutional Neural Networks for image classication


This summer school aim to provide an overview of the main machine learning methods and their application to geophysical, geological and environmental data, keeping a more practical flavour.

After the course the student will be able to use basic machine learning techniques applied to geosciences. The student will learn to identify which ML method is more suitable than others for the analysis of a particular datasets and to evaluate the performance of the used models. After the course the student will also have an overview of the main Machine Learning libraries (in particular SciKit-Learn, Tensorflow and Keras)

Who can apply

Graduate Students, Early-Stage Researchers, Professionals



Program Intensity



Admission Requirements

Basic knowledge of calculus, linear algebra and statistics (suggested).
Basic knowledge of Python Programming (compulsory).


  • The maximum number of participants is set to 25
  • The Summer School will be activates with at least 5 participants
  • Eligible candidates will be admitted following a "first come, first served" rule

Required Documents

  • Identity Document (*PASSPORT in case you are a foreign student*)
  • Enrolment Form
  • Curriculum Vitae

All the documents must be in pdf format, in order to upload them on the portal when required (See the "How to apply" page).




500 euro

Pay fees by Debit/Credit Card or PayPal online using the following form filling it with all the required data:

PagoPA - Payment Form 


  • International students without Italian Tax Code: please tick the box 'Anonymous' in order to disable the field 'Italian personal ID/VAT number'.
  • Please type your NAME and SURNAME next to the pre-filled text of the field 'Reason'
  • Please pay only after receiving the admission letter


Please write to the coordinator for further details.


3 - 7 July 2023

Application Deadline

1 April 2023


Dr. Francesco Grigoli Questo indirizzo email è protetto dagli spambots. È necessario abilitare JavaScript per vederlo. 

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Ultima modifica: Lun 17 Ott 2022 - 10:27

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