Ay 119: Astroinformatics - Spring 2026

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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Syllabus (pdf), including rules, expectations, etc.

Lectures / Discussion sessions: Tuesdays, 4-5 pm in 304 Cahill

Schedule by the week (subject to changes):

• Week 1 - George: Introduction
  • George's slides (pdf)
  • Assigned reading:
    • Dalya Baron: "Machine Learning in Astronomy: a practical overview" (arXiv:1904.07248), also this DOI
    • S. G. Djorgovski, A. A. Mahabal, M. J. Graham, K. Polsterer, A. Krone-Martins: "Applications of AI in Astronomy" (arXiv:2212.01493), also this DOI
    • History of artificial intelligence, from Wikipedia
  • Check out also:
    • "Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration", arXiv:2601.14235 or this link
    • "The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys", by M. Huertas-Company & F. Lanusse, Publ. Astron. Soc. Australia (2023), 40, e001, available at doi:10.1017/pasa.2022.55
    • George's lecture about the Astronomy Data Landscape, from the KISS short course, May 2019, and his slides (pdf) (obsolete by now, but it will give you a flavor)
    • Some interesting recent readings about the use of AI in science and academia:
      • Minas Karamanis, The machines are fine. I'm worried about us.
      • David Hogg, Why do we do astrophysics?
      • Matthew Schwartz, Vibe physics: The AI grad student
      • Anthropic blog has many interesting posts

• Week 2 - Matthew: ML Foundations
  • Matthew's slides (pdf)
  • George's slides (pdf) on Introduction to ML, based on Ciro Donalek's lectures from Caltech-JPL Big Data Analytics Virtual Summer School
  • Ashish's old slides (pdf)
  • Umaa Rebbapragada' ML Basics from AstroInformatics 2019: Video [starts at 1:15] * Slides (pdf)
  • Assignment:
    • Python notebook
  • Check out also:
    • D. Hogg & S. Villar, Is machine learning good or bad for the natural sciences?, Proc. 41st Intl. Conf. Machine Learning, PMLR 235:18439-18453, (2024), also arXiv/2405.18095
    • Wikipedia article on Machine Learning
    • Free book (pdf): "Introduction to Machine Learning" by L. Younes (2025), arXiv link * local pdf
    • Another free book (pdf): Introduction to Machine Learning by A. Smola and S.V.N. Vishwanathan, Cambridge University Press (2008)

• Week 3 - Matthew: Intro to DL: NN, CNNs, RNNs, Autoencoders

• Week 4 - Ashish: Classification

• Week 5 -Ashish: DL Applications in Astronomy

• Week 6 - Matthew: Advanced DL: Transformers

• Week 7 - Ashish: Advanced DL: LLM - application

• Week 8 - Matthew: Advanced DL: LLM - theory

• Week 9 - Santiago: Data visualization

Resources and Useful Links:

• There are many, many excellent resources online, including Wikipedia. Let us know if you find some that you really like, and we'll add them here
• International AstroInformatics Association resources page has many useful links (needs updating)
• We highly recommend Prof. Yaser Abu-Mostafa's class "Learning From Data" (aka CS 156): Lecture videos * slides
• Some free online textbooks:
  • AstroML Interactive Book
  • "Foundations of Machine Learning", 2nd ed., by M. Mohri, A. Rostamizadeh, & A. Talwalkar, MIT Press, local pdf
  • "The Little Book of Deep Learning", by F. Fleuret, local pdf
  • "Understanding Deep Learning", by J. D. Prince, MIT press, website * local pdf
  • "An Introduction to Statistical Learning with Applications in Python", by G. James et al., local pdf
  • "Statistical Machine Learning for Astronomy", by Y.-S. Ting: arXiv 2506.12230 * local pdf
  • "Alice's Adventures in a Differentiable Wonderland -- Volume I, A Tour of the Land", A primer on designing neural networks, by S. Scardapane: arXiv 2404.17625 * local pdf
  • "Probabilistic Machine Learning: An Introduction", by K. Murphy, MIT press, local pdf
  • "MIT class 18.657: Mathematics of Machine Learning", by P. Rigollet, website * local pdf
  • Caltech Libraries provide a free access to the O'Rilley books and videos (Caltech Access login required). This is a great resource.
• Old, but useful lectures from the Caltech-JPL Big Data Analytics Virtual Summer School
• Andrew Moore's ML tutorials from CMU