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Thematic CERN School of Computing on Machine Learning 2026

7–13 Jun 2026
Orkanen Building
Europe/Zurich timezone

CERN School of ComputingContact

Contribution List

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  1. 1. Registration at Hotel
    07/06/2026, 15:30

    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.

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  2. 47. Welcome, Logistics and self presentation
    Alberto Pace (CERN), Andrzej Nowicki (CERN), Kristina Gunne (CERN)
    07/06/2026, 17:00
  3. 4. Short walk in Malmö
    07/06/2026, 18:00
  4. 6. Opening session
    Alberto Pace (CERN)
    08/06/2026, 08:45
  5. 7. Machine learning methods: L1 Introduction to Statistics
    Toni Sculac (University of Split Faculty of Science (HR))
    08/06/2026, 09:45

    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.

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  6. 8. Announcements
    08/06/2026, 10:45
  7. 9. Machine learning methods: L2 Statistics and Machine Learning
    Toni Sculac (University of Split Faculty of Science (HR))
    08/06/2026, 11:30

    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.

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  8. 11. Machine learning methods: L3 Classical Machine Learning
    Toni Sculac (University of Split Faculty of Science (HR))
    08/06/2026, 13:30

    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...

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  9. 50. Technical setup
    08/06/2026, 14:35
  10. 12. Machine Learning methods: excercise 1
    Francesco Vaselli (Scuola Normale Superiore & INFN Pisa (IT))
    08/06/2026, 14:50
  11. 13. Machine Learning methods: excercise 2
    Francesco Vaselli (Scuola Normale Superiore & INFN Pisa (IT))
    08/06/2026, 16:15
  12. 10. Malmö: from harbor industry to startup-city
    08/06/2026, 17:15

    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.

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  13. 17. Machine Learning Methods: L4 Introduction to Deep Learning
    Toni Sculac (University of Split Faculty of Science (HR))
    09/06/2026, 08:45

    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...

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  14. 40. Machine Learning Methods: L5 Advanced Deep Learning
    Francesco Vaselli (Scuola Normale Superiore & INFN Pisa (IT))
    09/06/2026, 09:45
  15. 16. Announcements
    09/06/2026, 10:45
  16. 19. Machine Learning methods: exercise 3
    Francesco Vaselli (Scuola Normale Superiore & INFN Pisa (IT))
    09/06/2026, 11:30
  17. 14. Machine Learning in Accelerator Technologies: Machine Learning for particle accelerators
    Verena Kain (CERN)
    09/06/2026, 13:30

    Main use cases and applications

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  18. 15. Machine Learning in Accelerator Technologies: Bayesian Optimisation
    Verena Kain (CERN)
    09/06/2026, 14:30
  19. 20. Machine learning in accelerators: Exercise 1
    Michael Schenk (CERN), Verena Kain (CERN)
    09/06/2026, 16:00
  20. 18. Study time or daily sports
    09/06/2026, 17:00
  21. 37. Lightning talks - part 1 (Alin, Valdor)
    Hans Alin, Paula Desire Valdor (CERN)
    10/06/2026, 08:15
    • How to mitigate pile-up in the Light Dark Matter eXperiment, Hans Alin
    • ML for Lattice Design, Paula Desiré Valdor
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  22. 22. Machine Learning in Accelerators: Introduction to Reinforcement Learning
    Michael Schenk (CERN)
    10/06/2026, 08:45
  23. 23. Machine Learning in Accelerators: Advanced concepts for Reinforcement Learning
    Verena Kain (CERN)
    10/06/2026, 09:45
  24. 24. Announcements
    10/06/2026, 10:45
  25. 21. Machine learning in accelerators: Exercise 2
    Michael Schenk (CERN), Verena Kain (CERN)
    10/06/2026, 11:30
  26. 46. Transport to half day excursion
    10/06/2026, 13:45
  27. 26. Half day excursion
    10/06/2026, 14:45

    Snogeholmssjön

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  28. 49. Lightning talks - part 2 (Fuligno, Zarucki)
    Davide Fuligno (University of Pisa and INFN Trieste (IT)), Dr Mateusz Zarucki (CERN)
    11/06/2026, 08:15
    • Fast Simulation of the ALICE Zero Degree Calorimeter using Generative Models, Davide Fuligno
    • Revolutionising the CMS High-Level Trigger System at HL-LHC with Next Generation Triggers, Mateusz Zarucki
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  29. 48. Machine Learning in Data Analysis: Complex tasks, basic blocks. The importance of primitives in Machine Learning
    Francesco Vaselli (Scuola Normale Superiore & INFN Pisa (IT))
    11/06/2026, 08:45
  30. 28. Machine learning in Data Analysis: Introduction to Machine Learning for HEP, Anomaly detection and real time applications
    Dr Sofia Vallecorsa (CERN)
    11/06/2026, 09:45
  31. 30. Announcements
    11/06/2026, 10:45
  32. 45. Group photo
    11/06/2026, 11:00
  33. 29. Machine learning in Data Analysis: The data reconstruction step - a pattern recognition problem
    Dr Sofia Vallecorsa (CERN)
    11/06/2026, 11:30
  34. 34. Machine learning in Data Analysis: Exercise 1
    Francesco Vaselli (Scuola Normale Superiore & INFN Pisa (IT)), Dr Sofia Vallecorsa (CERN)
    11/06/2026, 13:30
  35. 35. Machine learning in Data Analysis: Exercise 2
    Dr Sofia Vallecorsa (CERN)
    11/06/2026, 14:30
  36. 25. Machine learning in accelerators: Exercise 3
    Verena Kain (CERN)
    11/06/2026, 16:00
  37. 32. Study time or daily sports
    11/06/2026, 17:00
  38. 31. Machine learning in Data Analysis: Generative Models for HEP
    Dr Sofia Vallecorsa (CERN)
    12/06/2026, 08:45
  39. 38. Machine learning in Data Analysis: Systematics in ML
    Dr Sofia Vallecorsa (CERN)
    12/06/2026, 09:45
  40. 39. Announcements
    12/06/2026, 10:45
  41. 36. Machine learning in Data Analysis: Exercise 3
    Dr Sofia Vallecorsa (CERN)
    12/06/2026, 11:30
  42. 41. Exam
    12/06/2026, 13:30
  43. 42. Closing ceremony
    Alberto Pace (CERN)
    12/06/2026, 15:00
  44. 43. Sports and leisure time
    12/06/2026, 16:00
  45. 44. Departures
    13/06/2026, 08:00
  46. 33. Coffee
  47. 27. Transport back to medILS
  48. 3. Transport to Split
  49. 5. Welcome dinner