These are the materials for the Mini Summer School in Machine Learning x Astronomy I taught at the Center for Computational Astrophysics of the Flatiron Institute in June 2019.
Videos of the lectures can be found on the YouTube channel of the Simons Foundation (search for "machine learning summer school").
Topics: Intro to ML, jargon, binary classification + metrics, decision trees
Data source
https://www.astroml.org/gatspy/datasets/rrlyrae.html
Packages: Numpy, pandas, sklearn, matplotlib, IPython, pydotplus
Topics: Metrics for classification problems, decision trees leftovers, bagging and boosting algorithm
Data source: Andrew Leung (https://iopscience.iop.org/article/10.3847/1538-4357/aa71af/meta).
Packages: Numpy, pandas, sklearn, matplotlib, scipy, time, warnings
Topics: Bagging and Boosting algorithms code example, Support Vector Machines, Nested cross validation and parameter optimization
New packages: none.
Topics: Quick look at implementation of nested cross validation, Regression, Clustering
New packages: skimage.
Topics: Clustering (cont’d), Dimensionality Reduction
Data (3 large files):
https://drive.google.com/open?id=1BK18eGAd580VH5F6BbB31pLF0DoOwmig
https://drive.google.com/open?id=1iQhjthdoYxG8NC-q9y9NKoWyzFEK3DbN
https://drive.google.com/open?id=1vKAXzusF7Ig0DoFAnHE1RXItzZKdH9HV
Data sources:
https://www.kaggle.com/c/galaxy-zoo-the-galaxy-challenge
https://www.astroml.org/user_guide/datasets.html
New packages: none.
https://www.youtube.com/user/SimonsFoundation/search?query=machine+learning+summer+school