Machine Learning Tutorial Session

Introduction

This is a set of tutorials for the Machine Learning Hands-on Advanced Tutorial Session (HATS). They are intended to show you how to build machine learning models in python (Keras/TensorFlow) and use them in your ROOT-based analyses. We will build event-level classifiers for differentiating VBF Higgs and standard model background 4 muon events and jet-level classifiers for differentiating boosted W boson jets from QCD jets.

Main notebooks in this tutorial

0. a-dataset-and-plot.ipynb: reading/writing datasets from ROOT files with uproot and plotting with matplotlib
1. b-dense.ipynb: building, training, and evaluating a fully connected (dense) neural network in Keras
2. b.1-dense-pytorch.ipynb: preprocessing CMS open data to build jet-images (optional)
3. c-conv2d.ipynb: preprocessing, building, training, and evaluating a 2D convolutional neural network in Keras

Setup

We will be setting up the environment using Miniconda wity Python3. This is wrapped in a Docker container for easy deployment.

Plain Miniconda3 setup

# currently it's setup for Mac, change the file if you want to run on Linux
source install_miniconda3.sh
# setup conda environmeent and install needed packages
source setup.sh

Docker setup

On its way -- need some help from Maria

Binder

You can launch notebooks in Binder for quick tests, but note this is not for resource-intensive computing:

Links

The accompanying lecture is here