Fourth ML-INFN Hackathon: Starting Level

Europe/Rome
http://cern.ch/go/pk7C (Zoom)

http://cern.ch/go/pk7C

Zoom

Direct link : https://cern.zoom.us/j/63455648268?pwd=T0UrY1VHMGhqK21BSFdXZC8rQ05xUT09 The Meeting ID is 634 5564 8268 with the passcode 11111
Description

 

Welcome to the fourth edition of the Machine Learning @ INFN (ML_INFN) starting level hackathon, dedicated to INFN Affiliates.

If you are looking for the previous editions, check the following links:

 

The course is to be considered as "starting level" for Machine Learning topics. The hackathon will be organized over 3 days, distributed as 

  1. General introduction on ML and on its use in INFN (including Clouds)
  2. Tutored hands-on of specific use cases, attempting to reach fully working products; a review of the ML utilization in specific use cases of INFN interest
  3. The hackathon, with participants working in groups trying to achieve a goal in the form of a realistic analyses. In the latter part, presentation of their work is expected and discussed among all the groups.

The use cases for third day ("hackathon")

Upon registration, users will be asked to express a first and second preference for a one of the use cases offered. We will try to

  • whenever possible, satisfy the preference in the order given
  • try to form groups with students with the full range of proficiencies, in order to allow for self-tutoring inside the groups

The list of available use cases for the hackathon are currently (there could be additions depending on the registration process and on the status of other opportunities):

  1. Signal / Background discrimination @ LHC: b Tagging AND Higgs Boson Searches
  2. autoencoders for VIRGO GW signal analysis
  3. classification of lesions in medical physics 

 

Prerequisites

Technical: access to INFN Cloud provided resources

NOTE: users will use INFN-Cloud resources, especially provided and configured for this event. This will not require any specific INFN-Cloud authorization, but INFN AAI credentials are needed. 

Please read and comply with the recommendations in the side menu "technical prerequisite".

Machine learning related knowledge

The hackathon is to be considered as entry level for Machine Learning. Still, some fluency in the tools to be used is a prerequisite and it is needed to speed up the tutoring. In particular, we expect:

  • some fluency in Python:
    • basic program flow, definition and use of variables
    • definition and utilization of functions
    • package management (import, install via pip)
  • some fluency in numpy:
    • arrays, basic operations, reshaping
  • some fluency on matplotlib:
    • how to define and plot a basic figure
  • some fluency on online python notebooks
    • (like SWAN, Jupyter, Google Colab)

In case you do not feel proficient enough in the previous items, we suggest a list of entry level tutorial / documentation we advise to read and test before the Hackathon, in order to be able to follow with profit the presentations and the hands-on. This can be found in the side menu, at the entry "Pre-tutorial Self-teaching".

Registration

Attending the hackathon is free and happens via the entry in the side menu.

The number of participants can be limited, depending on the tutoring and technical capabilities.

Upon registration, we will require:

  • INFN Affiliation
  • Research interests
  • Proficiency level in Machine Learning and related tools
  • Specific interests in Machine learning  techniques
  • The preferred use case for the third day (first and second choice)

Additional Material

Please consider visiting again this page from time to time: we will add more resources for the hackathon and related projects as soon as they become available,.

Participants
  • Alberto Arzenton
  • Alberto Manfreda
  • Alessandro Alberto Oliva
  • Alessandro Longo
  • Andrea Mancuso
  • Andrea Paccagnella
  • Antonino Troja
  • Arman Zafaranchi
  • Davide Gerosa
  • Davide Serafini
  • Deborah Chilà
  • Edoardo Leandri
  • Fabrizio Capuani
  • Federica Fabrizi
  • Federico Nardi
  • Francesca Bucci
  • Francesca Camagni
  • Francesca Mainas
  • Francesca Passalacqua
  • Francesco D'Angelo
  • Gioacchino Alex Anastasi
  • Giovanni Cavallotto
  • Giovanni Tripodo
  • Giulia Fumagalli
  • Giuseppe Di Somma
  • Giuseppe Sapienza
  • Gloria Maria Cicciari
  • Hanaan Rahmah Shafi
  • Ilaria Panichi
  • Laura Panebianco
  • Laura Salutari
  • Lorenzo Bellizzi
  • Lorenzo Mobilia
  • Maria Antonietta Palaia
  • Maria Assiduo
  • Maria Irene Tenerani
  • Martina Moglioni
  • Matteo Da Valle
  • Mattia Romeo
  • Matus Balogh
  • Mina Maghami Moghim
  • Naomi Marchini
  • Noemi Pino
  • Pietro Di Gangi
  • Rosa Petrini
  • Sebastiana Maria Puglia
  • Stefano Truzzi
  • Stephanie de Beer
    • 09:00 09:20
      Introduction and logistics 20m
      Speaker: Lucio Anderlini (Istituto Nazionale di Fisica Nucleare)
    • 09:20 11:15
      Introduction: General Introduction to Machine Learning
    • 11:15 11:30
      Break 15m
    • 11:30 12:30
      Introduction: INFN Cloud for ML-INFN
    • 12:30 13:45
      Lunch break 1h 15m
    • 13:45 16:15
      Hands-on: Getting started with python and numpy (+ ...) on the cloud infrastructure
      • 13:45
        Hands-on - Playing with CERN Open Data with numpy, pandas and pyplot 1h 15m
        Speaker: Lucio Anderlini (FI)
      • 15:00
        Introduction to keras 30m
        Speakers: Andrea Rizzi (INFN Pisa), Andrea Rizzi (PI)
      • 15:30
        Hands-on - Introduction to keras 45m
        Speakers: Andrea Rizzi (PI), Andrea Rizzi (INFN Pisa)
    • 09:00 09:40
      Machine Learning Applications for Gravitational Wave science 40m
      Speaker: Elena Cuoco (Istituto Nazionale di Fisica Nucleare)
    • 09:40 10:15
      ML Basics: hands-on on a simple categorization example in HEP 35m
      Speaker: Lucio Anderlini (FI)
    • 10:15 10:30
      Coffee break 15m
    • 10:30 11:30
      Hands-on: Convolutional Neural Networks
      Conveners: Andrea Rizzi (INFN Pisa), Andrea Rizzi (Istituto Nazionale di Fisica Nucleare)
    • 11:30 12:30
      Real applications of ML in INFN activities - Image Restoration in heritage 1h
      Speaker: Alessandro Bombini (FI)
    • 12:30 13:10
      Machine Learning Applications for Medical Physics 40m
      Speaker: Piernicola Oliva (University of Sassari and INFN Cagliari)
    • 13:10 14:20
      Lunch break 1h 10m
    • 14:20 16:20
      Hands-on: Continuation and finalization of hands on
    • 09:00 09:40
      Hackathon introduction: Description of the exercises
      • 09:00
        Discrimination of hadronic jets from light and heavy flavours @ LHC 10m
        Speaker: Matteo Malucchi (INFN Pisa)
      • 09:10
        Signal/background discrimination for the VBF Higgs four lepton decay channel with the CMS experiment 10m
        Speaker: Walaa Elmetenawee (Istituto Nazionale di Fisica Nucleare)
      • 09:20
        Autoencoders for VIRGO GW signal analysis 10m
        Speakers: Lucia Papalini (Istituto Nazionale di Fisica Nucleare), Michele Vacatello (Istituto Nazionale di Fisica Nucleare)
      • 09:30
        Classification of lesions in medical physics 10m
        Speaker: Francesca Lizzi (Istituto Nazionale di Fisica Nucleare)
    • 09:40 13:00
      Hands-on: Hackathon
      • 09:40
        b-Tagging at CMS with Recurrent Neural Networks 3h 20m
        Speakers: Andrea Rizzi (INFN Pisa), Andrea Rizzi (Istituto Nazionale di Fisica Nucleare), Francesco Vaselli (INFN Pisa), Matteo Malucchi (INFN Pisa)
      • 09:40
        Classification of lesions in medical physics 3h 20m
        Speakers: Alessandra Retico (Istituto Nazionale di Fisica Nucleare), Andrea Berti (Istituto Nazionale di Fisica Nucleare), Camilla Scapicchio (Istituto Nazionale di Fisica Nucleare), Francesca Brero (Istituto Nazionale di Fisica Nucleare), Ian Postuma (Istituto Nazionale di Fisica Nucleare), Dr Lorenzo Marini (Istituto Nazionale di Fisica Nucleare), Raffaella Cabini (Istituto Nazionale di Fisica Nucleare), Sara Saponaro (Istituto Nazionale di Fisica Nucleare)
      • 09:40
        Processing Gravitational Waves with Machine Learning 3h 20m
        Speakers: Luca Rei (Istituto Nazionale di Fisica Nucleare), Lucia Papalini (Istituto Nazionale di Fisica Nucleare), Marco Serra (Istituto Nazionale di Fisica Nucleare), Massimiliano Razzano (University of Pisa and INFN-Pisa), Michele Vacatello (Istituto Nazionale di Fisica Nucleare)
      • 09:40
        Selecting Higgs Canidates with Deep Neural Networks 3h 20m
        Speakers: Ms Brunella D'Anzi (INFN - Bari), Giorgia Miniello (Istituto Nazionale di Fisica Nucleare), Nicola De Filippis (BA), Walaa Elmetenawee (Istituto Nazionale di Fisica Nucleare)
    • 13:00 14:30
      Lunch break 1h 30m
    • 14:30 16:30
      Hackathon introduction: Discussion on exercises