Astroinformatics Summer School 2022

Organized by Penn State Center for Astrostatistics

Slides

Program

"Day 0": Getting Started/Fundamentals of Machine Learning

Optimization (github)

• Gradient Descent Lab
  This lab serves two purposes: providing intution about the gradient descent algorithm for optimizing functions and making sure that students are able to access the servers for the workshop.

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Day 1: Fundamentals of Machine Learning

Regression & Classification (github)

• Philosophy of Astroinformatics: slides
• Linear Regression Lab: slides
• Logistic Regression Lab: slides
• Application: Classifying High-redshift Quasars I Lab

Data Mining

• Intro to Databases & SQL (slides)
• SQL Lab:
  • Getting started with SciServer.org instructions: (text) & (video)
  • SQL Lab instructions

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Day 2: Machine Learning in Practice

Regularized Regression for Machine Learning (github)

• Regularized Regression Lab: slides

Dimensional Reduction (github)

• Slides
• Intro to PCA Lab
• Kernel PCA & SVMs Lab
• Application: Classifying High-redshift Quasars II Lab
• Application: Galaxy classification Lab

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Day 3: Hierarchical Modeling & Intro to Neural Networks

Bayesian Computing (github)

• Monte Carlo Integration Lab: slides
• Application: Hierarchical Model of Galaxy Evolution: slides
• Intro to Probabilistic Programming Languages Lab
• Hierarchical Modeling via a PPL

Neural Networks (slides & github)

• Application: Neural Networks Lab (Classifying High-redshift Quasars III)

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Day 4: Modern Machine Learning Methods

Variational Inference (slides & github)

• Application: Image classificaiton

Scientific Machine Learning (github)

• Scientific Machine Learning Lab: slides

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Day 5: High-Performance Computing

High-Performance Computing (github)

• Slides:

  • Conepts
  • Reources

• Linear Algebra with GPUs Lab

• Neural Networks with GPUs Lab

• Putting the Peice Together: [slides](Putting the Peice Together Ford.pdf)

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Website & registration

Additional Resources

What is Astroinformatics? The Place of Machine Learning in Astronomy & Astrophysics

• Astroinformatics: A 21st Century Approach to Astronomy (Borne 2009)
• Data Mining and Machine Learning in Astronomy (Ball & Brunner 2010)
• Machine learning and the physical sciences* (Carleo et al. 2019)
• Machine learning in Astronomy (Baron 2019)

Professional Organizations

• IAU Commission B3 Astroinformatics and Astrostatistics
• AAS Working Group on Astroinformatics & Astrostatistics
• International Astroinformatics Association