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Materials for the Mini Summer School in Machine Learning x Astro taught at CCA in Summer '19
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README.md

README.md

MLSummerSchool_CCA19

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").

Outline

June 6

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

June 13

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

June 20

Topics: Bagging and Boosting algorithms code example, Support Vector Machines, Nested cross validation and parameter optimization

New packages: none.

June 25

Topics: Quick look at implementation of nested cross validation, Regression, Clustering

New packages: skimage.

June 27

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.

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