Thematic CERN School of Computing on Machine Learning 2026

7 Jun 2026, 14:00 → 13 Jun 2026, 11:00 Europe/Zurich

Room D222 (Orkanen Building)

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

The 19^th Thematic CERN School of Computing (tCSC Machine Learning 2026) will take place on June 7-13. 

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 Orkanen 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 2026

• 13 February, application opens
• March 25, application closes
• April 1, invitations sent to selected students
• April 23, participation fee deadline

 

Photo: CSC Organisers

Booklet

Photos and Video Gallery

Sunday 7 June

1 Registration at Hotel

We will greet students to the school at the Comfort Hotel Malmö
Carlsgatan 10 C, 211 20 Malmö, Sweden
Location: https://maps.app.goo.gl/fayc5oVwx9WtBgqJ8

Check in at the hotel is possible at all times, in case your room is not yet availble you can store your luggage at the hotel reception.

2 Welcome, Logistics and self presentation

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

3 Short walk in Malmö

19:00 Welcome to Malmö dinner

Kockums fritid
Västra Varvsgatan 8
211 11 Malmö

Monday 8 June

4 Opening session

Speaker: Alberto Pace (CERN)

2026-06-07-Director-Slides-Opening-v04-Short.pdf

2026-06-07-Director-Slides-Opening-v04-Short.pptx

MAU-TS-SDRC-260608.pdf

MAU-TS-SDRC-260608.pptx

5 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))

6 Announcements

Daily updates Monday 8 June.pdf

Daily updates Monday 8 June.pptx

11:00 Coffee break

7 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

8 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))

14:30 Move to Exercise room

9 Technical setup

10 Machine Learning methods: excercise 1

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

dataA.csv

solutions1.ipynb

tCSC_MachineLearning_Exercise_Instructions.pdf

tCSC_ml_set_1.pdf

15:50 Coffee break

11 Machine Learning methods: excercise 2

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

dataB.csv

solutions2.ipynb

tCSC_ml_set_2.pdf

12 Malmö: from harbor industry to startup-city

We will take a walk in the area around the University and discover the history of Malmö and discover the new start up landscape. We will end the walk by having dinner together.

19:00 Dinner at Slottsträdgårdens café

Tuesday 9 June

13 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))

14 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))

Link to slides

Link to slides

15 Announcements

11:00 Coffee break

16 Machine Learning methods: exercise 3

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

Challenge link

Codabench Q&A

Codabench Q&A

dataC.csv

DeepNN-2.ipynb

solutions3a.ipynb

tCSC_advanced_exercise.pdf

tCSC_ml_set_3a.pdf

12:30 Lunch

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

Main use cases and applications

Speaker: Verena Kain (CERN)

18 Machine Learning in Accelerator Technologies: Bayesian Optimisation

Speaker: Verena Kain (CERN)

15:30 Coffee break

19 Machine learning in accelerators: Exercise 1

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

Exercises_ML4acc.pdf

20 Study time or daily sports

19:00 Dinner at Kockums Fritid

Wednesday 10 June

21 Lightning talks - part 1 (Alin, Valdor)

• How to mitigate pile-up in the Light Dark Matter eXperiment, Hans Alin
• ML for Lattice Design, Paula Desiré Valdor

Speakers: Hans Alin, Paula Desire Valdor (CERN)

22 Machine Learning in Accelerators: Introduction to Reinforcement Learning

Speaker: Michael Schenk (CERN)

23 Machine Learning in Accelerators: Advanced concepts for Reinforcement Learning

Speaker: Verena Kain (CERN)

24 Announcements

11:00 Coffee break

25 Machine learning in accelerators: Exercise 2

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

12:30 Lunch

26 Transport to half day excursion

27 Half day excursion

Snogeholmssjön

Thursday 11 June

28 Lightning talks - part 2 (Fuligno, Spasic, Zarucki)

• Fast Simulation of the ALICE Zero Degree Calorimeter using Generative Models, Davide Fuligno
• Detecting Anomalies in Cloud Logs with XGBoost and a Modified Firefly Algorithm, Veljko Spasic
• Revolutionising the CMS High-Level Trigger System at HL-LHC with Next Generation Triggers, Mateusz Zarucki

Speakers: Davide Fuligno (University of Pisa and INFN Trieste (IT)), Dr Mateusz Zarucki (CERN), Veljko Spasic

29 Machine Learning in Data Analysis: Complex tasks, basic blocks. The importance of primitives in Machine Learning

Speaker: Francesco Vaselli (Scuola Normale Superiore & INFN Pisa (IT))

Link to slides

Link to slides

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

Speaker: Dr Sofia Vallecorsa (CERN)

31 Announcements

32 Group photo

11:05 Coffee break

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

Speaker: Dr Sofia Vallecorsa (CERN)

12:30 Lunch

34 Machine learning in Data Analysis: Exercise 1

Speaker: Dr Sofia Vallecorsa (CERN)

CSC_exercises.pdf

Link to Ilaria's exercises

35 Machine learning in Data Analysis: Exercise 2

Speaker: Dr Sofia Vallecorsa (CERN)

15:30 Coffee break

36 Machine learning in accelerators: Exercise 3

Speaker: Verena Kain (CERN)

37 Study time or daily sports

19:00 Dinner at Blue boat

Swedish Summer Buffet dinner

Friday 12 June

38 Machine learning in Data Analysis: Generative Models for HEP

Speaker: Dr Sofia Vallecorsa (CERN)

39 Machine learning in Data Analysis: Systematics in ML

Speaker: Dr Sofia Vallecorsa (CERN)

40 Announcements

11:00 Coffee + walk to Niagara building

41 Machine learning in Data Analysis: Exercise 3

Speaker: Dr Sofia Vallecorsa (CERN)

12:30 Lunch

42 Exam

14:30 Break

43 Closing ceremony

Speaker: Alberto Pace (CERN)

44 Sports and leisure time

18:00 Closing event at Kockums fritid

Saturday 13 June

45 Departures