Ay 119: Astroinformatics - Spring 2022

Open to everyone interested - students from any field are welcome.

Instructors:

• George Djorgovski [djorgovski]
• Ashish Mahabal [ashish]
• Matthew Graham [mjg]
• Santiago Lombeda [santiago]
• [name] at caltech dot edu

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Read this syllabus first: how it will work (pdf)

Discussion sessions: Wednesdays, 2-3 pm in 211 Cahill

Facebook discussion group

Schedule by the week:

The Week of March 28: George: Introduction

• George's lecture about the Astronomy Data Landscape, from the KISS short course, May 2019, and his slides (pdf)
• Umaa Rebbapragada's tutorial on Machine Learning Basics from Astroinformatics 2019, and her slides (pdf)
• Explore the IAIA resources page and the links therein
• Explore the past Astroinformatics conferences. Most have videos and slides of the talks posted, including many useful reviews and tutorial talks. In particular, check out the tutorials linked under the ‘Program’ at Astroinformatics 2019.

Resources and Useful Links:

• International AstroInformatics Association resources page has many useful links. This should probably be your first stop.

• Caltech Libraries provide a free access to the O'Rilley books and videos (Caltech Access login required). This is a great resource.

• Dalya Baron's very useful Machine Learning in Astronomy: a Practical Overview on arXiv.

• We will be using a lot of lectures from the Caltech-JPL Big Data Analytics Virtual Summer School

Last update: March 30, 2022, SGD