Topical Lectures "Machine Learning"

8 Jun 2022, 09:30 → 10 Jun 2022, 18:00 Europe/Amsterdam

Z009 (Nikhef/CWI)

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

Wednesday, 8 June

1 Morning session I

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

Speaker: Juan Rojo

ML-TopicalLectures-Rojo.pdf

11:00 Coffee break

2 Morning session II

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

Speaker: Juan Rojo

12:45 Lunch break

3 Afternoon session I

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

Speaker: Tanjona Radonirina Rabemananjara (NIKHEF & VU Amsterdam)

15:15 Tea break

4 Afternoon session II

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

Speaker: Tanjona Radonirina Rabemananjara (NIKHEF & VU Amsterdam)

Thursday, 9 June

5 Morning session I

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

Speaker: Juan Rojo

ML-TopicalLectures-Rojo-Day2.pdf

11:00 Coffee break

6 Morning session II

machine learning for LHCb/flavor physics

Speaker: Jacco de Vries

MLinLHCb_topicallect_090622.pdf

12:45 Lunch break

7 Afternoon session I

hands-on tutorials with Jupyter notebooks (unsupervised learning)

Speaker: Tanjona Radonirina Rabemananjara (NIKHEF & VU Amsterdam)

15:15 Tea break

8 Afternoon session II

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

Speaker: Jacco de Vries

Friday, 10 June

9 Morning session I

CNNs in gravitational wave physics

Speaker: Amit Reza (Nikhef Amsterdam)

Reza-Nikhef-Deep-Learning-GW-Presentation.pdf

11:00 Coffee break

10 Morning session II

machine learning for LHC high-pT physics

Speaker: Johnny Raine

ML High pT Physics at LHC.pdf

12:45 Lunch break

11 Afternoon session I

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

Speaker: Amit Reza (Nikhef Amsterdam)

15:15 Tea break

12 Afternoon session II

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

Speaker: Johnny Raine (CERN)

17:00 Drinks!