Advanced Computing and Machine Learning

Europe/Zurich
Virtual (Microsoft Teams) (Instituto Galego de Física de Altas Enerxías (IGFAE))

Virtual (Microsoft Teams)

Instituto Galego de Física de Altas Enerxías (IGFAE)

Andrey Ustyuzhanin (Yandex School of Data Analysis (RU)) , Gonzalo Diaz Lopez (IGFAE - USC) , Jose Angel Hernando Morata (Universidade de Santiago de Compostela (ES)) , Josh Renner (Univ. of Valencia and CSIC (ES)) , Xabier Cid Vidal (Instituto Galego de Física de Altas Enerxías)
Description

Artificial Neural Networks (ANNs) are computational models able to learn from previously provided datasets, constituting a different paradigm than classical computation algorithms in which tasks are run sequentially. This ability has allowed ANNs to produce accurate solutions to complex problems, such as image or sound recognition, that significantly outperform solutions based on classical computing. Their development has been aided by rapid improvements in hardware capabilities over the last two decades and is nowadays an active field of study whose applications extend far beyond academic research. 
 
In this course, organized by the Instituto Galego de Física de Altas Enerxías (IGFAE), basic methods in machine learning and ANNs will be introduced at a conceptual and practical level, as well as some interesting applications in Particle Physics and industry. The course will take place virtually through Microsoft Teams in the mornings of 25th, 26th, 27th of May and 1st, 2nd and 3rd of June. It is aimed at researchers, teachers, CIT professionals and students.
 
Overview:
- Introduction to Neural Networks (Days 1, 2, 3)
- Applications in Particle Physics (Days 4 and 5)
- Applications in industry (Day 6)
 
Important requirements:
- Basic knowledge of statistics, Python and Jupyter Notebooks

Connection details:
- We will have live events in Microsoft Teams (no registration or account needed) and we will also create a team for you to join the hands-on sessions. This will be announced in due time.

To join the course every day at 9:00, click in the following links:

Day 1 [25/05/20]

Day 2 [26/05/20]

Day 3 [27/05/20]

 

Please, bear in mind we will be using the interactive rooms the second week of the course. You should be part of the specific Team created for this. If you've got problems joining, please let us know.

 

 

Connection details:
- Hands-on session link:

https://colab.research.google.com/github/HSE-LAMBDA/MLatUSC-2020

NEXT activity (Day 4):

 

Participants
  • Adrian Casais Vidal
  • Adrián Cora
  • Alba Costa
  • Alberto García Martín-Caro
  • Alberto Rivadulla Sánchez
  • Alberto Saborido Patiño
  • Alberto Usón Andrés
  • Alejandro Vilar López
  • Alexandre Brea Rodriguez
  • Alicia Mosquera Rodriguez
  • Anxo Lema Saavedra
  • Araceli Domínguez Bugarín
  • Asier Pereiro Castro
  • Brais Palmeiro Pazos
  • Caetano Eirea Orro
  • Carlos A. Salgado
  • Carlos Hervés Carrete
  • Carmen Pajares
  • Cibran Santamarina Rios
  • Clara Dominguez Chapela
  • Daniele Musso
  • David Ferro Costas
  • Diego Martínez H.
  • Elisabet Galiana Baldo
  • Eloi Pazos Rial
  • Emilio Martinez-Nunez
  • Francisco García López
  • Gabriel García
  • Gonzalo Martínez Lema
  • Imanol Corredoira Fernandez
  • Iván Conde Leborán
  • Iván Martínez Suárez
  • Javier Garcia Pazos
  • Javier Mas Sole
  • Jose Luis Rodriguez Sanchez
  • juan ruso
  • Julian Lomba Castro
  • Julio Iglesias Fajin
  • Lois Fernandez Miguez
  • Lorena Dieste Maroñas
  • Manuel Fdez-Arroyo
  • Marcos Romero Lamas
  • Marcos Seoane
  • Maria Ruiz Ortega
  • Marta Rodríguez
  • Martín Calvelo Souto
  • María Rodríguez
  • Mauro Saavedra Golán
  • Miguel Fernández Gómez
  • Miguel Huidobro
  • Miguel Vidal
  • Monica Gonzalez
  • Mónica Canabal
  • Pablo Baladrón Rodríguez
  • Pablo Suárez Vieites
  • Petja Paakkinen
  • Rita Neves
  • Rubén del Mazo Rodríguez
  • Rubén Paredes García
  • Samuel Ferrero Losada
  • Sara Neira Castro
  • Saúl López Soliño
  • Sergio Calo Oliveira
  • Uxia Veleiro
  • Víctor López Pardo