This repository contains lecture notes and Juypter notebooks for the Astronomy Data and Compute Services (ADACS) - Introduction to Machine Learning workshop run at the Astronomical Society of Australia's (ASA) 2017 Annual Scientific Meeting in Canberra, Australia.
The workshop materials have been prepared by:
from the Curtin Institute for Computation at Curtin University in Perth, Australia.
A basic knowledge of python is assumed for this workshop. i.e. knowledge of basic data structures, operations and how to write a script. The notebooks have been developed to be compatible with Python 2.7x and 3.x and, may require some Python packages to be updated to the most recent version (e.g. scikit-learn).
The dataset used in this workshop is compiled from Galaxy Zoo DR1 and the Sloan Digital Sky Survey (SDSS) (using the DR9 SQL search).
The notebooks are descriptive and comprehensive enough to be attempted at your own pace - a solution notebook is also provided. The lecture notes explain the intuition behind how different machine learning algorithms work.
A gitter chatroom has also been created for the workshop related discussions - https://gitter.im/ADACS-ML-workshop/Lobby.
- Data preparation
- Exploratory data analysis
- Classification
- Cross validation
- Learning curves
- Model tuning
- Reporting
- Regression
- Clustering
- Dimensionality reduction
This work is made available under the Creative Commons Attribution 4.0 International License.