Topical Lectures "Machine Learning"

Europe/Amsterdam
Z009 (Nikhef/CWI)

Z009

Nikhef/CWI

Description

For the hands-on tutorial sessions

  • Participants will bring their own laptop

  • Exercises for the hands-on session should be presented in terms of Python Jupyter notebooks. We will run in Google Collab (no need to lose time with local installations). 

  • Jupyter notebooks:

    • Stochastic gradient descent: link

    • Regression in machine learning: link

    • Classification of four-top events at the LHC: link

    • Likelihood free inference for astroparticle physics: link

    • LHCb flavour physics tutorial on anomaly detection: collab link

    • Machine Learning for high-pt processes at the LHC: link

    • Deep learning for gravitational wave physics: link

For online remote connection (only morning lectures)

Zoom: https://vu-live.zoom.us/my/juanrojo?pwd=TWJ0YUpvVTFuN1M4TU9RQnRzTjlpdz09

Recordings of the morning lectures:

  • Recording of the lecture on Wednesday 8th July 2022: Introduction to Machine Learning Part I (Juan Rojo): link
  • Recording of the lecture on Thursday 9th July 2022: Introduction to Machine Learning Part II (Juan Rojo): link
  • Recording of the lecture on Thursday 9th July 2022:  Machine Learning in LHCb (Jacco de Vries): link
  • Recording of the lecture on Friday 10th July 2022: ML for high-pT physics (Johnny Raine): link
  • Recording of the lecture on Friday 10th July 2022: ML for Gravitational Waves (Amit Reza): link
    • 9:30 AM 11:00 AM
      Morning session I 1h 30m Z009

      Z009

      Nikhef/CWI

      General introduction to Machine Learning, including supervised (regression + classification) and unsupervised learning and (deep) neural networks

      Speaker: Juan Rojo
    • 11:00 AM 11:15 AM
      Coffee break 15m Z009

      Z009

      Nikhef/CWI

    • 11:15 AM 12:45 PM
      Morning session II 1h 30m Z009

      Z009

      Nikhef/CWI

      General introduction to Machine Learning, including supervised (regression + classification) and unsupervised learning and (deep) neural networks

      Speaker: Juan Rojo
    • 12:45 PM 1:45 PM
      Lunch break 1h
    • 1:45 PM 3:15 PM
      Afternoon session I 1h 30m Z009

      Z009

      Nikhef/CWI

      hands-on tutorials with Jupyter notebooks (supervised learning regression + classification)

      Speaker: Tanjona Radonirina Rabemananjara (NIKHEF & VU Amsterdam)
    • 3:15 PM 3:30 PM
      Tea break 15m Z009

      Z009

      Nikhef/CWI

    • 3:30 PM 5:00 PM
      Afternoon session II 1h 30m Z009

      Z009

      Nikhef/CWI

      hands-on tutorials with Jupyter notebooks (supervised learning regression + classification)

      Speaker: Tanjona Radonirina Rabemananjara (NIKHEF & VU Amsterdam)
    • 9:30 AM 11:00 AM
      Morning session I 1h 30m Z009

      Z009

      Nikhef/CWI

      advanced topics in machine learning: convolutional networks, adversarial learning, reinforcement learning

      Speaker: Juan Rojo
    • 11:00 AM 11:15 AM
      Coffee break 15m Z009

      Z009

      Nikhef/CWI

    • 11:15 AM 12:45 PM
      Morning session II 1h 30m Z009

      Z009

      Nikhef/CWI

      machine learning for LHCb/flavor physics

      Speaker: Jacco de Vries
    • 12:45 PM 1:45 PM
      Lunch break 1h
    • 1:45 PM 3:15 PM
      Afternoon session I 1h 30m Z009

      Z009

      Nikhef/CWI

      hands-on tutorials with Jupyter notebooks (unsupervised learning)

      Speaker: Tanjona Radonirina Rabemananjara (NIKHEF & VU Amsterdam)
    • 3:15 PM 3:30 PM
      Tea break 15m Z009

      Z009

      Nikhef/CWI

    • 3:30 PM 5:00 PM
      Afternoon session II 1h 30m Z009

      Z009

      Nikhef/CWI

      hands-on tutorials with Jupyter notebooks (ML for LHCb/flavor physics)

      Speaker: Jacco de Vries
    • 9:30 AM 11:00 AM
      Morning session I 1h 30m Z009

      Z009

      Nikhef/CWI

      CNNs in gravitational wave physics

      Speaker: Amit Reza (Nikhef Amsterdam)
    • 11:00 AM 11:15 AM
      Coffee break 15m Z009

      Z009

      Nikhef/CWI

    • 11:15 AM 12:45 PM
      Morning session II 1h 30m Z009

      Z009

      Nikhef/CWI

      machine learning for LHC high-pT physics

      Speaker: Johnny Raine
    • 12:45 PM 1:45 PM
      Lunch break 1h
    • 1:45 PM 3:15 PM
      Afternoon session I 1h 30m Z009

      Z009

      Nikhef/CWI

      hands-on tutorials with Jupyter notebooks (CNNs in GWs)

      Speaker: Amit Reza (Nikhef Amsterdam)
    • 3:15 PM 3:30 PM
      Tea break 15m Z009

      Z009

      Nikhef/CWI

    • 3:30 PM 5:00 PM
      Afternoon session II 1h 30m Z009

      Z009

      Nikhef/CWI

      hands-on tutorials with Jupyter notebooks (ML in ATLAS)

      Speaker: Johnny Raine (CERN)
    • 5:00 PM 6:00 PM
      Drinks! 1h Z009

      Z009

      Nikhef/CWI