Machine learning tutorial

By Michelle Lochner

To get a copy of this repository type git clone https://github.com/MichelleLochner/ml-tutorials.git in the command line or click "Clone or download" and click "download zip" if you don't have git installed.

Key files:

machine_learning_notes.pdf -> The notes from the lecture (without the answers)
tutorial-basic.ipynb -> A very simple tutorial illustrating some of the concepts from the lecture tutorial-supernovae.ipynb -> The main tutorial using supernova classification as an example tutorial-galaxies.ipynb -> A different example with some raw spectroscopic galaxy data for you to play with tutorial-deep-learning.ipynb -> A very simple and non-exhaustive deep learning example with pytorch

N.B. MAKE SURE THE WHOLE NOTEBOOK WORKS BEFORE ARRIVING AT A WORKSHOP

For example, tutorial-supernovae.ipynb will need to download some models the first time you run it. If you try do this without an internet connection, it will fail. Check that all the existing code works beforehand.

Running the code

The requirements to run this tutorial code are

dependencies:

• python>=3
• astropy>=1.1.2
• jupyter>=1.0.0
• jupyter lab (recommended)
• matplotlib>=1.5.1
• ipympl (recommended)
• numpy>=1.11.0
• scikit-learn>=0.18.1
• scipy>=0.17.0
• iminuit>=0.12
• sncosmo>=1.3.0
• pytorch, torchvision (if you want to run the deep learning tutorial)

All these packages can be installed with pip3 install <package name> --user.

Running the tutorial

Type jupyter notebook tutorial_supernova.ipynb into the command line after activating the environment. All the tutorial notebooks can be run this way.

Deep Learning tutorial

The tutorial on deep learning is extremely simple, just to give you an idea of how to get started. You need to install pytorch, instructions in the notebook. Warning: Deep learning is very slow without a GPU, don't try to do anything too complicated without one!