g2net - 2nd Training School - Machine Learning and Signal processing for Time Series Analysis

CA17137 - A network for Gravitational Waves, Geophysics and Machine Learning

Teaching material for 'g2net - 2nd Training School - Machine Learning and Signal processing for Time Series Analysis' held at the University of Malta, Valletta Campus, Malta, 9-13 March 2020.

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TimeTable

Module Code	Topic
SP	Signal Processing
ML	Machine Learning
DL	Deep Learning
GRAV	Gravitational Data Talk
GEO	Geophysical Data Talk
HACK	ML Hackaton

Lectures

• SP - Signal Processing - Eftim Zdravevski
  • SP1 - Preparing Environment
  • SP2 - Introduction and Visualization
  • SP3 - Preparing GW Data
• ML - Machine Learning - Christopher Zerafa, Luigia Petre
  • CZ - ML1 - Introduction to Python & Machine Learning
  • CZ - ML2_3 - Classification vs Regression and Metrics
  • CZ - ML4 - Model Selection and Feature Engineering
  • LP - ML5_6 - Trees And Ensembles
  • LP - ML7 - Naïve Bayes
  • LP - ML8 - Support Vector Machines
• DL - Deep Learning - Luca Antiga
  • Lectures
    • DL1_2 - Neural networks and back-propagation & PyTorch basics
    • DL3_4 - Deep learning in PyTorch & Deep learning for g2net data
  • Notebooks
    • 1.1-PyTorch-Basics
    • 1.2-Linear-Regression
    • 1.3-Multi-Class-Logisitc-Regression
    • 2.1-Convolutional-Neural-Networks
    • 2.2-Pretrained-ResNet-Imagenet
    • 2.3-TransferLearning-MNIST
    • 2.4-Finetuning-Hymenoptera
    • 2.5-CharRNN
• GRAV - Gravitational Data Talk - Michal Bejger, Jade Powell
  • MB - GRAV1_2 - Introduction to Gravitational Waves
  • JP - GRAV3_4 - Gravitational Wave Data Analysis with Machine Learning
• GEO - Geophysical Data Talk - Tomek Bulik, Gregory Beroza
  • TB - GEO1_2
    • Slides
    • Recording
  • GB - GEO3 - Deep Learning Applied to Earthquake Signals
    • Slides
    • Recording
• HACK - Machine Learning Hackaton - Christopher Zerafa, Filip Morawski, Alberto Iess
  • Platform: g2net - Hack
  • Data: g2net_malta_hackaton
  • Deadline: Friday 13/03/2020 13:00 GMT+1

Resources

• Deep Learning with PyTorch

Photos

You can find photo album with pictures here.

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