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Thematic CERN School of Computing on Machine Learning 2025

8–14 Jun 2025
Niagara Building
Europe/Zurich timezone

CERN School of ComputingContact

Contribution List

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  1. 1. Registration at Hotel
    08/06/2025, 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 and self presentation
    Alberto Pace (CERN), Andrzej Nowicki (CERN), Kristina Gunne (CERN)
    08/06/2025, 17:00
  3. 4. Short walk in Malmö
    08/06/2025, 18:00
  4. 6. Opening session
    Alberto Pace (CERN), Ms Annika Annerby Jansson (Region Skane)
    09/06/2025, 08:45
  5. 7. Machine learning methods: L1 Introduction to Statistics
    Toni Sculac (University of Split Faculty of Science (HR))
    09/06/2025, 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
    09/06/2025, 10:45
  7. 9. Machine learning methods: L2 Statistics and Machine Learning
    Toni Sculac (University of Split Faculty of Science (HR))
    09/06/2025, 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))
    09/06/2025, 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. 12. Machine Learning methods: excercise 1
    Francesco Vaselli (Scuola Normale Superiore & INFN Pisa (IT)), Toni Sculac (University of Split Faculty of Science (HR))
    09/06/2025, 14:35
  10. 13. Machine Learning methods: excercise 2
    Francesco Vaselli (Scuola Normale Superiore & INFN Pisa (IT)), Toni Sculac (University of Split Faculty of Science (HR))
    09/06/2025, 16:00
  11. 10. Malmö: from harbor industry to startup-city
    09/06/2025, 17:00

    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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  12. 14. Machine Learning in Accelerator Technologies: Machine Learning for particle accelerators
    Verena Kain (CERN)
    10/06/2025, 08:45

    Main use cases and applications

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  13. 15. Machine Learning in Accelerator Technologies: Bayesian Optimisation
    Verena Kain (CERN)
    10/06/2025, 09:45
  14. 16. Announcements
    10/06/2025, 10:45
  15. 17. Machine Learning Methods: L4 Introduction to Deep Learning
    Francesco Vaselli (Scuola Normale Superiore & INFN Pisa (IT)), Toni Sculac (University of Split Faculty of Science (HR))
    10/06/2025, 11:30

    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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  16. 40. Machine Learning Methods: L5 Advanced Deep Learning
    Francesco Vaselli (Scuola Normale Superiore & INFN Pisa (IT)), Toni Sculac (University of Split Faculty of Science (HR))
    10/06/2025, 13:30
  17. 20. Machine learning in accelerators: Exercise 1
    Michael Schenk (CERN), Verena Kain (CERN)
    10/06/2025, 14:35
  18. 21. Machine learning in accelerators: Exercise 2
    Michael Schenk (CERN), Verena Kain (CERN)
    10/06/2025, 16:00
  19. 18. Study time or daily sports
    10/06/2025, 17:00
  20. 37. Lightning talks - part 1
    Athar Khodabakhsh, Jaroslaw Szumega (CERN EP-DT-DD, Mines ParisTech (FR))
    11/06/2025, 08:15

    Athar Khodabakhsh - The Value of Naive Questions in Multidisciplinary XAS Research
    Jaroslaw Szumega - Machine Learning and Autoencoding Techniques in High-Energy Physics Irradiation Experiments

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  21. 22. Machine Learning in Accelerators: Introduction to Reinforcement Learning
    Michael Schenk (CERN)
    11/06/2025, 08:45
  22. 23. Machine Learning in Accelerators: Advanced concepts for Reinforcement Learning
    Verena Kain (CERN)
    11/06/2025, 09:45
  23. 24. Announcements
    11/06/2025, 10:45
  24. 19. Machine Learning methods: exercise 3
    Francesco Vaselli (Scuola Normale Superiore & INFN Pisa (IT)), Toni Sculac (University of Split Faculty of Science (HR))
    11/06/2025, 11:30
  25. 46. Transport by bus from Comfort Hotel to Snogeholm
    11/06/2025, 13:45
  26. 26. Destination Snogeholm
    11/06/2025, 14:45

    Canoeing and hiking excursion, including BBQ dinner by the fireplace at the lake of Snogeholm.

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  27. 49. Lightning talks - part 2
    Giacomo Tangari (Sapienza Universita e INFN, Roma I (IT)), Joao Ramiro, Laurence Matthew Wroe (CERN)
    12/06/2025, 08:15

    Giacomo Tangari - CLEAR, operational status and machine learning opportunities
    Laurence Matthew Wroe - ML Applications in Compact Electron Linacs
    Joao Ramiro - Life as a librarian

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  28. 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))
    12/06/2025, 08:45
  29. 28. Machine learning in Data Analysis: Introduction to Machine Learning for HEP, Anomaly detection and real time applications
    Dr Sofia Vallecorsa (CERN)
    12/06/2025, 09:45
  30. 30. Announcements
    12/06/2025, 10:45
  31. 45. Group photo
    12/06/2025, 11:00
  32. 29. Machine learning in Data Analysis: The data reconstruction step - a pattern recognition problem
    Dr Sofia Vallecorsa (CERN)
    12/06/2025, 11:30
  33. 34. Machine learning in Data Analysis: Exercise 1
    Dr Sofia Vallecorsa (CERN)
    12/06/2025, 13:30
  34. 35. Machine learning in Data Analysis: Exercise 2
    Dr Sofia Vallecorsa (CERN)
    12/06/2025, 14:30
  35. 25. Machine learning in accelerators: Exercise 3
    Verena Kain (CERN)
    12/06/2025, 16:00
  36. 32. Study time or daily sports
    12/06/2025, 17:00
  37. 31. Machine learning in Data Analysis: Generative Models for HEP
    Dr Sofia Vallecorsa (CERN)
    13/06/2025, 08:45
  38. 38. Machine learning in Data Analysis: Systematics in ML
    Dr Sofia Vallecorsa (CERN)
    13/06/2025, 09:45
  39. 39. Announcements
    13/06/2025, 10:45
  40. 36. Machine learning in Data Analysis: Exercise 3
    Dr Sofia Vallecorsa (CERN)
    13/06/2025, 11:30
  41. 41. Exam
    13/06/2025, 13:30
  42. 42. Closing ceremony
    Alberto Pace (CERN)
    13/06/2025, 15:00
  43. 43. Sports and leisure time
    13/06/2025, 16:00
  44. 44. Departures
    14/06/2025, 08:00
  45. 33. Coffee
  46. 27. Transport back to medILS
  47. 3. Transport to Split
  48. 5. Welcome dinner