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