Organized by Penn State Center for Astrostatistics

• 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.

• Linear Regression Lab
• Logistic Regression Lab
• Application: Classifying High-redshift Quasars I Lab

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

• Regularized Regression Lab

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

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

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

• Application: Image classificaiton

• Scientific Machine Learning Lab

• Linear Algebra with GPUs Lab
• Neural Networks with GPUs Lab

• 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)

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