This repository contains lecture notes and Juypter notebooks for the Curtin Institute for Computation (CIC) - Introduction to Machine Learning workshop run at the Curtin University in Perth, Australia.
The workshop materials have been prepared by:
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.
- 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.