ML4Physics@Ljubljana school

Europe/Zurich
Lecture rooms F1, F2 (Faculty of Mathematics and Physics, University of Ljubljana)

Lecture rooms F1, F2

Faculty of Mathematics and Physics, University of Ljubljana

Jadranska ul. 19, 1000 Ljubljana, Slovenia
Borut Paul Kersevan (Jozef Stefan Institute (SI)), Ilaria Brivio (University & INFN Bologna), Jernej Fesel Kamenik (Jozef Stefan Institute (SI)), Karolos Potamianos (University of Warwick (GB)), Martino Borsato (Universita & INFN, Milano-Bicocca (IT)), Pietro Govoni (Universita & INFN, Milano-Bicocca (IT))
Description

School description

The objective of the Machine Learning for Physics school, organised by the COMETA COST Action, the SMARTHEP European training network, theย SMASH MSCN COFUND, Institute Jozef Stefan and the University of Ljubljana is to provide participants from diverse backgrounds a solid knowledge on cutting edge machine learning topics.

In order to be accessible to many, the first three days of the school will be organised in two parallel sessions: one focussing on fundamentals, and the other on real-time applications of machine learning. Each single day will feature specific subjects and students will be able to select their own attending plan according to the daily schedule.

The second part of the programme will be plenary and it will focus on machine learning applications for data analysis and physics use cases.

Each day will feature morning lectures, afternoon exercises, and keynote talks, outreach or social activities in the evening.

Confirmed Topics - Trainers

  • Machine Learning fundamentals - Lorenzo Moneta (CERN)
  • Real-Time Machine Learning - Thea Aarrestad (ETH Zurich)
  • Unfolding with Machine Learning with physics examples - Sofia Palacios Schweitzer (University Heidelberg)
  • Uncertainties on Machine Learning predictions - Luigi Favaro (University Heidelberg and UCLouvain)
  • Statistics and Machine Learning - Lydia Brenner (NIKHEF)
  • Explainability in Machine Learning - Saso Dzeroski (IJS Ljubljana)
  • Foundation Models - Anna Hallin (Hamburg University)

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Confirmed Keynote Speakers

  • Uroลก Seljak (UC Berkley)
  • Nenad Tomasev (DeepMind)

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Program and Dates

The school will start on June the 26th and finish on July the 2nd 2025.ย 

Participation Costs and Grants

The school will not have participation fees,ย and registrations will close on May the 4th.

Grants offered by the school will be available for a limited number of students, and will reimburse travel and accommodation costs. The grant request will have to be submitted through the registration form.

Accommodation for students at discounted rates is available at the DIC hostel. Please see the page "Lodging" for instuctions on how to book.

Organising parties

  • Local Organising Committee: Jernej F. Kamenik, Borut Kersevan, Tadej Novak, Aleks Smolkovic
  • Scientific board: Martino Borsato, Ilaria Brivio, Alessandra Cappati, Riccardo Finotello, Pietro Govoni Jernej F. Kamenik, Borut Kersevan, Claudius Krause, Karolos Potamianos
  • Coordination: Pietro Govoni

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Contacts: DELETETHISml-ljubljana-25@cern.ch

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Registration
Registration for lecturers and tutors
Registration for local participants
Registration for students
    • 1
      Machine Learning Fundamentals 1
      Speaker: Lorenzo Moneta (CERN)
    • 2
      Real Time Machine Learning 1
      Speaker: Thea Aarrestad (ETH Zurich (CH))
    • 10:30
      break
    • 3
      Machine Learning Fundamentals 1
      Speaker: Lorenzo Moneta (CERN)
    • 4
      Real Time Machine Learning 1
      Speaker: Thea Aarrestad (ETH Zurich (CH))
    • 12:30
      lunch
    • 5
      Machine Learning Fundamentals Exercises 1
      Speaker: Lorenzo Moneta (CERN)
    • 6
      Real Time Machine Learning Exercises 1
      Speaker: Thea Aarrestad (ETH Zurich (CH))
    • 15:30
      break
    • 7
      Machine Learning Fundamentals Exercises 1
      Speaker: Lorenzo Moneta (CERN)
    • 8
      Real Time Machine Learning Exercises 1
      Speaker: Thea Aarrestad (ETH Zurich (CH))
    • 9
      Machine Learning Fundamentals 2
      Speaker: Lorenzo Moneta (CERN)
    • 10
      Real Time Machine Learning 2
      Speaker: Thea Aarrestad (ETH Zurich (CH))
    • 10:30
      break
    • 11
      Machine Learning Fundamentals 2
      Speaker: Lorenzo Moneta (CERN)
    • 12
      Real Time Machine Learning 2
      Speaker: Thea Aarrestad (ETH Zurich (CH))
    • 12:30
      lunch
    • 13
      Machine Learning Fundamentals Exercises 2
      Speaker: Lorenzo Moneta (CERN)
    • 14
      Real Time Machine Learning Exercises 2
      Speaker: Thea Aarrestad (ETH Zurich (CH))
    • 15:30
      break
    • 15
      Machine Learning Fundamentals Exercises 2
      Speaker: Lorenzo Moneta (CERN)
    • 16
      Real Time Machine Learning Exercises 2
      Speaker: Thea Aarrestad (ETH Zurich (CH))
    • 17
      Keynote 1
      Speaker: Uros Seljak (UC Berkley)
    • 18
      Machine Learning Fundamentals 3
      Speaker: Lorenzo Moneta (CERN)
    • 19
      Real Time Machine Learning 3
      Speaker: Thea Aarrestad (ETH Zurich (CH))
    • 10:30
      break
    • 20
      Machine Learning Fundamentals 3
      Speaker: Lorenzo Moneta (CERN)
    • 21
      Real Time Machine Learning 3
      Speaker: Thea Aarrestad (ETH Zurich (CH))
    • 12:30
      lunch
    • 22
      Machine Learning Fundamentals Exercises 3
      Speaker: Lorenzo Moneta (CERN)
    • 23
      Real Time Machine Learning Exercises 3
      Speaker: Thea Aarrestad (ETH Zurich (CH))
    • 15:30
      break
    • 24
      Machine Learning Fundamentals Exercises 3
      Speaker: Lorenzo Moneta (CERN)
    • 25
      Real Time Machine Learning Exercises 3
      Speaker: Thea Aarrestad (ETH Zurich (CH))
    • 09:00
      free
    • 26
      Explainability in machine learning
      Speaker: Saso Dzeroski
    • 27
      Foundation models
      Speaker: Anna Hallin (University of Hamburg)
    • 15:30
      break
    • 28
      Explainability in machine learning exercises
      Speaker: Saso Dzeroski
    • 29
      Foundation models exercises
      Speaker: Anna Hallin (University of Hamburg)
    • 30
      Machine Learning and Unfolding
      Speaker: Sofia Palacios Schweitzer
    • 10:30
      break
    • 31
      Machine Learning and Unfolding
      Speaker: Sofia Palacios Schweitzer
    • 12:30
      lunch
    • 32
      Machine Learning and Unfolding Exercises 1
      Speaker: Sofia Palacios Schweitzer
    • 15:30
      break
    • 33
      Machine Learning and Unfolding Exercises 1
      Speaker: Sofia Palacios Schweitzer
    • 34
      Social Dinner
    • 35
      Statistics and ML
      Speaker: Lydia Brenner (Nikhef National institute for subatomic physics (NL))
    • 10:30
      break
    • 36
      Statistics and ML
      Speaker: Lydia Brenner (Nikhef National institute for subatomic physics (NL))
    • 12:30
      lunch
    • 37
      Statistics and ML - Exercises
      Speaker: Lydia Brenner (Nikhef National institute for subatomic physics (NL))
    • 15:30
      break
    • 38
      Statistics and ML - Exercises
      Speaker: Lydia Brenner (Nikhef National institute for subatomic physics (NL))
    • 39
      Keynote 2
    • 40
      Uncertainty quantification
      Speaker: Luigi Favaro (Universite Catholique de Louvain (UCL) (BE))
    • 10:30
      break
    • 41
      Uncertainty quantification
      Speaker: Luigi Favaro (Universite Catholique de Louvain (UCL) (BE))
    • 12:30
      lunch
    • 42
      Uncertainty quantification Exercises
      Speaker: Luigi Favaro (Universite Catholique de Louvain (UCL) (BE))
    • 15:30
      break
    • 43
      Uncertainty quantification Exercises
      Speaker: Luigi Favaro (Universite Catholique de Louvain (UCL) (BE))