Thematic CERN School of Computing on Machine Learning 2025

8 Jun 2025, 14:00 → 14 Jun 2025, 11:00 Europe/Zurich

Malmö University

Alberto Pace (CERN), Andrzej Nowicki (CERN), Kristina Gunne (CERN)

The 17^th Thematic CERN School of Computing (tCSC Machine Learning 2025) will take place on June 8-14. 

The school will focus on the theme of Machine Learning and Artificial Intelligence applied to Data Analysis and Accelerator Technology. The programme will offer 22 hours of lectures and hands-on exercises, as well as student presentation sessions.

This school is organized by CERN in collaboration with the Computer Science Department at Malmö University. The school will take place in Malmö, Sweden, and be hosted in the Niagara building of the University in the city center of Malmö.

The school is proposed to people working in academia and research institutes, who as part of their jobs use or want to explore using Machine Learning techniques. The school will cover the basics of Machine Learning before moving to advance state of the art techniques.

This school is aimed at postgraduate (i.e. minimum of Bachelor degree or equivalent) students, engineers and scientists with a few years' experience in particle physics, in computing, or in related fields. We welcome applications from all countries and nationalities.

Important dates 2025

• 24 January, application opens
• March 12, application closes
• March 26, invitations sent to selected students
• April 23, participation fee deadline

 

Photo: CSC Organisers

poster_a4.pdf

Sunday 8 June

1 Registration at Hotel

Comfort Hotel Malmö
Carlsgatan 10 C, 211 20 Malmö, Sweden
Location: https://maps.app.goo.gl/fayc5oVwx9WtBgqJ8

2 Walk around Malmö

19:30 Welcome dinner

Monday 9 June

3 opening session

Speaker: Alberto Pace (CERN)

4 Machine learning methods: L1 Introduction to Statistics

In this lecture we will go over key concepts in statistics which are the cornerstone of mathematical foundation of Machine Learning. We will define frequentistic and Bayesian probabilities, learn what is a PDF. We will also discuss parameter estimation with the Maximum Likelihood method and finish with the definition of Confidence Intervals.

Speaker: Toni Sculac (University of Split Faculty of Science (HR))

5 Announcements

11:00 Coffee break

6 Machine learning methods: L2 Statistics and Machine Learning

We start this lecture with unfolding and hypothesis testing, another two key concepts from statistics. Key part of the lecture is the Neyman-Person lemma that paves a clear path for the needs of Machine Learning in statistics.

Speaker: Toni Sculac (University of Split Faculty of Science (HR))

12:30 Lunch

7 Machine learning methods: L3 Classical Machine Learning

We continue tackling the problem of trying to know the likelihood ratio with the use of Classical Machine Learning. We try to solve it by brute force and then we move to Machine Learning techniques. We start with a Kernel Density Estimators. We continue by defining what is a decision tree, what is a leaf and we study how it works on a very simple example. We go further and explain the difference between classification and regression, as well as the need for pruning, bagging, and boosting. This main goal of this lecture is to remove the idea of the “black-box approach" and understand all of the details of a decision tree.

Speaker: Toni Sculac (University of Split Faculty of Science (HR))

8 Machine Learning methods: excercise 1

Speakers: Francesco Vaselli (Scuola Normale Superiore & INFN Pisa (IT)), Toni Sculac (University of Split Faculty of Science (HR))

15:30 Coffee break

9 Machine Learning methods: excercise 2

Speakers: Francesco Vaselli (Scuola Normale Superiore & INFN Pisa (IT)), Toni Sculac (University of Split Faculty of Science (HR))

10 Study time or daily sports

19:00 Dinner

Tuesday 10 June

11 Machine Learning in Accelerator Technologies: Machine Learning for particle accelerators

Main use cases and applications

Speaker: Verena Kain (CERN)

12 Machine Learning in Accelerator Technologies: Bayesian Optimisation

Speaker: Verena Kain (CERN)

13 Announcements

11:00 Coffee break

14 Machine Learning Methods: L4 Introduction to Deep Learning

We introduce the concept of a Neural Network (NN) and study their application with a single-neuron network. This again allows us to avoid the "black-box approach" and really understand the key concepts of how a NN works. We discuss activation functions and how the NN learns with the help of the loss functions and backpropagation. We finish by discussing the basic idea of a Deep Neural Network and basic training concepts.

Speakers: Francesco Vaselli (Scuola Normale Superiore & INFN Pisa (IT)), Toni Sculac (University of Split Faculty of Science (HR))

12:30 Lunch

15 Machine Learning Methods: L5 Advanced Deep Learning

Speakers: Francesco Vaselli (Scuola Normale Superiore & INFN Pisa (IT)), Toni Sculac (University of Split Faculty of Science (HR))

14:30 Coffee break

16 Machine learning in accelerators: Exercise 1

Speakers: Michael Schenk (CERN), Verena Kain (CERN)

17 Machine learning in accelerators: Exercise 2

Speakers: Michael Schenk (CERN), Verena Kain (CERN)

18 Study time or daily sports

19:00 Dinner

Wednesday 11 June

19 Machine Learning in Accelerators: Introduction to Reinforcement Learning

Speaker: Michael Schenk (CERN)

20 Machine Learning in Accelerators: Advanced concepts for Reinforcement Learning

Speaker: Verena Kain (CERN)

21 Announcements

11:00 Coffee break

22 Machine Learning methods: exercise 3

Speakers: Francesco Vaselli (Scuola Normale Superiore & INFN Pisa (IT)), Toni Sculac (University of Split Faculty of Science (HR))

12:30 Lunch

23 Afternoon activity

19:00 Dinner

Thursday 12 June

24 Machine learning in Data Analysis: Introduction to Machine Learning for HEP, Anomaly detection and real time applications

Speaker: Dr Sofia Vallecorsa (CERN)

25 Machine learning in Data Analysis: The data reconstruction step - a pattern recognition problem

Speaker: Dr Sofia Vallecorsa (CERN)

26 Announcements

27 Group photo

11:05 Coffee break

28 Machine learning in Data Analysis: Generative Models for HEP

Speaker: Ilaria Luise (CERN)

12:30 Lunch

29 Machine learning in Data Analysis: Exercise 1

Speakers: Ilaria Luise (CERN), Dr Sofia Vallecorsa (CERN)

30 Machine learning in Data Analysis: Exercise 2

Speakers: Ilaria Luise (CERN), Dr Sofia Vallecorsa (CERN)

15:30 Coffee break

31 Machine learning in accelerators: Exercise 3

Speaker: Verena Kain (CERN)

32 Study time or daily sports

19:00 Dinner

Friday 13 June

33 Lightning talks

34 Machine learning in Data Analysis: Systematics in ML

Speaker: Ilaria Luise (CERN)

35 Announcements

11:00 Coffee

36 Machine learning in Data Analysis: Exercise 3

Speakers: Ilaria Luise (CERN), Dr Sofia Vallecorsa (CERN)

12:30 Lunch

37 Exam

14:30 Break

38 Closing ceremony

Speaker: Alberto Pace (CERN)

39 Sports and leisure time

18:00 Boule games and dinner at Malmö Boulebar

Saturday 14 June

40 Departures