Machine Learning and Applications to Physics

Europe/Madrid
Aula M2 (Facultad de Ciencias Físicas, Universidad Complutense de Madrid)

Aula M2

Facultad de Ciencias Físicas, Universidad Complutense de Madrid

P/ Ciencias 1 E-28040, Madrid
Joaquin L. Herraiz (Universidad Complutense de Madrid) , Raquel Molina Peralta (Universidad Complutense de Madrid - IPARCOS) , Daniel Nieto Castaño (IPARCOS (UCM))
Description

Machine Learning is an expanding field with enormous potential for Physics applications. In particular, Deep-Learning methods can be used for improved data processing, signal analysis, and modeling. Some of these methods have already been successfully applied in fields ranging from particle physics and astrophysics to medical physics, but much of their scope and possible applications in physics are yet to be explored.

A proper understanding and a fair use of the machine-learning toolkit will be more and more necessary in research. In this context, the Institute of Particle and Cosmos Physics (IPARCOS) seeks to promote the knowledge of this type of techniques among its researchers, and among the university community in general.

This first "Machine Learning and Applications to Physics" workshop has a twofold objective:

  • To carry out an introductory course on Machine Learning techniques, including theory and hands-on sessions, for all those interested in starting to use them. 
  • To learn from both national and international researchers, the use they are making of Machine Learning techniques in various areas of Physics.

It is going to be carried out in three days. In the morning there will be a theory session and talks from different experts working on this field and the afternoon will be devoted to a hands-on session.

The introductory course is structured as follows:

It is mainly focused on researchers from the Faculty of Physical Sciences of the UCM, but it is also open to researchers from other universities and research centers.

Participants
  • Adrian Belarra
  • Adrián Bembibre Fernández
  • Adrián Casado Turrión
  • Adrián González
  • Alberto Dominguez
  • Alberto Muñoz
  • Alejandra Aguirre-Santaella
  • Alejandro López Montes
  • Alfredo Cuesta
  • Alicia González López
  • Amaia Villa
  • Ana Arribas Gil
  • Ana menendez
  • Andrea Espinlsa Rodríguez
  • Andrea Vioque Rodríguez
  • Andres Baquero
  • Aris Villacorta
  • Begoña García-Conde Navarro
  • Borja Sanchez
  • Bryan Zaldivar
  • Carlos Cifuentes San Roman
  • Carlos Ortega
  • Carlos Quezada Calonge
  • Carlos Rodríguez
  • Clara Freijo Escudero
  • Clara Álvarez Luna
  • Daniel de Andres Hernandez
  • Daniel Eduardo Borrajo Gutiérrez
  • Daniel Gutiérrez Reyes
  • Daniel Morcuende
  • Daniel Nieto Castaño
  • David González González
  • David Gordo
  • Diego Esteban
  • Ekaterina Lobacheva
  • Emilio Gómez Marfil
  • Felipe J. Llanes-Estrada
  • Fernando Arias
  • Fernando Gil
  • Fernando Labarga Ávalos
  • Francesca Scarcella
  • FRANCISCO MONROY
  • Grettel Victoria Motola Ortiz
  • Hector Villarrubia-Rojo
  • Horacio Lopez Menendez
  • Héctor Rueda Ricarte
  • Ilaria Paga
  • Ilaria Paga
  • Isabel Gallego Llorente
  • Isidoro González-Adalid
  • JALAL TOUNLI NEMRI
  • Javier Moreno-Gordo
  • Javier Ruano
  • Joaquin Lopez Herraiz
  • John Kim Dinh Hoang
  • Jose Alberto Ruiz Cembranos
  • Jose Manuel Alarcon
  • JOSE MANUEL Udias Moinelo
  • Juan Bernete Medrano
  • Juan J. Sanz Cillero
  • Juan Mena
  • Juan Rojo
  • Luis J. Garay
  • Luis Mario Fraile
  • Marcos Rodriguez Chamorro
  • Martín Rodríguez
  • María Evangelina Lope Oter
  • Mercedes Hernández
  • Mercedes Martin Benito
  • Mercedes Riveira
  • Miguel Aparicio Resco
  • Miriam Garcia Santa Maria
  • Nadezhda Chirkova
  • Nerea Encina Baranda
  • Norberto Malpica
  • Pablo Galve Lahoz
  • Pablo Peñil
  • PABLO RIVERA PÉREZ
  • Patricia Martínez
  • Paula del Burgo
  • Paula Ibáñez
  • Pedro Larrañaga
  • Pedro Zufiria
  • Raquel Galazo García
  • Raquel Molina Peralta
  • Rita Neves
  • Rocío Navarro
  • Samuel Escrig
  • Santi Roca-Fàbrega
  • Silvia Bueno
  • Teodor Borislavov Vassilev
  • Tjark Miener
  • Tomás Sánchez Sánchez-Pastor
  • Victor Alcayne Aicua
  • Victor Vallalid
  • Viviana Gammaldi
  • Víctor González
  • Ángel López Corps
  • Ángela García Argumánez
    • 09:00 10:30
      Machine learning (theory): Introduction to statistical learning (I) Aula M2

      Aula M2

      Facultad de Ciencias Físicas, Universidad Complutense de Madrid

      P/ Ciencias 1 E-28040, Madrid
      Convener: Prof. Pedro José Zufiria Zatarain (Universidad Politécnica de Madrid)
      • 09:00
        Introduction to statistical learning (I) 1h 30m
        Speaker: Prof. Pedro José Zufiria Zatarain (Universidad Politécnica de Madrid)
    • 10:30 11:00
      Coffee break 30m
    • 11:00 13:00
      Applications beyond Physics: Session I Aula M2

      Aula M2

      Facultad de Ciencias Físicas, Universidad Complutense de Madrid

      P/ Ciencias 1 E-28040, Madrid
    • 13:00 14:00
      Lunch 1h
    • 14:00 15:30
      Machine learning (theory): Introduction to statistical learning (II) Aula M2

      Aula M2

      Facultad de Ciencias Físicas, Universidad Complutense de Madrid

      P/ Ciencias 1 E-28040, Madrid
      Convener: Prof. Pedro José Zufiria Zatarain (Universidad Politécnica de Madrid)
      • 14:00
        Introduction to statistical learning (II) 1h 30m
        Speaker: Prof. Pedro José Zufiria Zatarain (Universidad Politécnica de Madrid)
    • 15:30 16:00
      Coffee break 30m
    • 16:00 18:00
      Machine learning (hands-on): Session I Aula M2

      Aula M2

      Facultad de Ciencias Físicas, Universidad Complutense de Madrid

      P/ Ciencias 1 E-28040, Madrid
      Convener: Prof. Pedro José Zufiria Zatarain (Universidad Politécnica de Madrid)
    • 09:00 10:30
      Machine learning (theory): Session III Aula M2

      Aula M2

      Facultad de Ciencias Físicas, Universidad Complutense de Madrid

      P/ Ciencias 1 E-28040, Madrid
      Convener: Prof. Juan Rojo (VU University (Amsterdam))
      • 09:00
        Supervised learning 45m

        Model fitting and polynomial regression,
        regularisation and cross-validation, gradient descent and its variants

        Speaker: Prof. Juan Rojo (VU University)
      • 09:45
        Deep neural networks 45m

        Deep neural networks, backpropagation, regularisation of neural networks

        Speaker: Prof. Juan Rojo (VU University)
    • 10:30 11:00
      Coffee break 30m
    • 11:00 12:20
      Applications: Session I Aula M2

      Aula M2

      Facultad de Ciencias Físicas, Universidad Complutense de Madrid

      P/ Ciencias 1 E-28040, Madrid
    • 12:20 13:00
      Machine learning at IPARCOS: Session I Aula M2

      Aula M2

      Facultad de Ciencias Físicas, Universidad Complutense de Madrid

      P/ Ciencias 1 E-28040, Madrid
      • 12:20
        ML IN NUCLEAR PHYSICS & NUCLEAR MEDICINE 15m
        Speaker: Joaquin L. Herraiz (Universidad Complutense de Madrid)
      • 12:35
        Machine learning meets astroparticle physics @ IPARCOS 15m
      • 12:50
        Model selection for pion photoproduction 10m
        Speaker: Raquel Molina Peralta (Universidad Complutense de Madrid - IPARCOS)
    • 13:00 14:00
      Lunch 1h
    • 14:00 15:30
      Machine learning (theory): Session IV Aula M2

      Aula M2

      Facultad de Ciencias Físicas, Universidad Complutense de Madrid

      P/ Ciencias 1 E-28040, Madrid
      Convener: Prof. Juan Rojo (VU University (Amsterdam))
      • 14:00
        Supervised learning for classification and logistic regression 30m
        Speaker: Prof. Juan Rojo (VU University)
      • 14:30
        Dimensional reduction and data visualisation 30m
        Speaker: Prof. Juan Rojo (VU University)
      • 15:00
        Unsupervised learning: clustering & ensemble methods 30m
        Speaker: Prof. Juan Rojo (VU University)
    • 15:30 16:00
      Coffee break 30m
    • 16:00 18:00
      Machine learning (hands-on): Session II Aula M2

      Aula M2

      Facultad de Ciencias Físicas, Universidad Complutense de Madrid

      P/ Ciencias 1 E-28040, Madrid
      Convener: Prof. Juan Rojo (UV Amsterdam)
    • 09:00 10:30
      Bayesian methods: Introduction Aula M2

      Aula M2

      Facultad de Ciencias Físicas, Universidad Complutense de Madrid

      P/ Ciencias 1 E-28040, Madrid
    • 10:30 11:00
      Coffee break 30m Aula M2

      Aula M2

      Facultad de Ciencias Físicas, Universidad Complutense de Madrid

      P/ Ciencias 1 E-28040, Madrid
    • 11:00 12:30
      Bayesian methods: Inference Aula M2

      Aula M2

      Facultad de Ciencias Físicas, Universidad Complutense de Madrid

      P/ Ciencias 1 E-28040, Madrid
    • 12:30 13:00
      Bayesian methods: Bayesian linear regression Aula M2

      Aula M2

      Facultad de Ciencias Físicas, Universidad Complutense de Madrid

      P/ Ciencias 1 E-28040, Madrid
    • 13:00 14:00
      Lunch 1h Aula M2

      Aula M2

      Facultad de Ciencias Físicas, Universidad Complutense de Madrid

      P/ Ciencias 1 E-28040, Madrid
    • 14:00 15:00
      Keynote: Aprendizaje Automático: Pasado, Presente y Futuro Aula M2

      Aula M2

      Facultad de Ciencias Físicas, Universidad Complutense de Madrid

      P/ Ciencias 1 E-28040, Madrid
      Convener: Prof. Pedro Larrañaga (Universidad Politécnica de Madrid)
    • 15:00 15:30
      Coffee break 30m Aula M2

      Aula M2

      Facultad de Ciencias Físicas, Universidad Complutense de Madrid

      P/ Ciencias 1 E-28040, Madrid
    • 15:30 17:00
      Bayesian neural networks: Training Aula M2

      Aula M2

      Facultad de Ciencias Físicas, Universidad Complutense de Madrid

      P/ Ciencias 1 E-28040, Madrid
      • 15:30
        Training Bayesian neural networks 1h
        Speaker: Nadezhda Chirkova (HSE University)
      • 16:30
        Bayesian sparsification of neural networks 30m
        Speaker: Nadezhda Chirkova (HSE University)
    • 17:00 18:00
      Bayesian neural networks: Practical seminar Aula M2

      Aula M2

      Facultad de Ciencias Físicas, Universidad Complutense de Madrid

      P/ Ciencias 1 E-28040, Madrid
      • 17:00
        Practice on Bayesian sparsification of neural networks 1h
        Speaker: Nadezhda Chirkova (HSE University)
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