TALENT School 2021 | ONLINE

19 July 2021 — 30 July 2021

Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this Nuclear Talent course is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists and nuclear physicists in particular. We will start with some of the basic methods from supervised learning, such as various regression methods before we move into deep learning methods for both supervised and unsupervised learning.



  • Daniel Bazin (Michigan State University)
  • Morten Hjorth-Jensen (Michigan State University & University of Oslo)
  • Michelle Kuchera (Davidson College)
  • Sean Liddick (Michigan State University)
  • Raghuram Ramanujan (Davidson College)

Student Coordination

  • Morten Hjorth-Jensen (Michigan State University & University of Oslo)


Registration available from 19/05/2021 until 30/06/2021.


Barbara Gazzoli (ECT*)


The course will be taught as an online intensive course of duration of two weeks, with a total time of 20 h of lectures and 10 h of exercises, questions and answers. Videos and digital learning material will be made available one week before the course begins. It is possible to work on a final assignment of 2 weeks of work. The total load will be approximately 80 hours, corresponding to 5 ECTS in Europe.

Time and Activity

  • 2pm-4pm (Central European time=CET) Lectures, project relevant information and directed exercises
  • 5pm-6pm (CET) Questions and answers, Computational projects, exercises and hands-on sessions

The tentative (prone to revisions) lecture plan is as follows

Week 1

Week 2

Privacy Notice

Pursuant to art. 13 of EU Regulation No. 2016/679 – General Data Protection Regulation and as detailed in the Privacy Policy for FBK-ECT* event’s participants, we inform you that the event will be recorded and disclosed on the FBK-ECT* institutional channels. In order not to be filmed or recorded, you can disable the webcam and/or mute the microphone during virtual events or inform the FBK-ECT* staff who organize the public event beforehand.