Ay 119: Astroinformatics - Spring 2021

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

Instructors:

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

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Read this first: how it will work (pdf)

Discussion sessions: Wednesdays, 10-11 am PDT Zoom link [code=astroinfo]

Facebook discussion group

Schedule by the week:

• The Week of March 29: George: Introduction
  • George's lecture about the Astronomy Data Landscape, from the KISS short course, May 2019, and his slides (pdf)
  • Alternative/supplementary viewing: George's introductory lecture "Exploring Space in the Cyberspace", from the IAC XXX Winter School "Big Data Analysis in Astronomy", Nov. 2018, and his slides (pdf) [Note: a lot of repetition from the previous link]
  • Explore the IAIA resources page
  • Explore the past Astroinformatics conferences. Most have videos and slides of the talks posted, including many useful review and tutorial talks
  • FB Astroinformatics page

• The Week of April 5: Ashish: Best Programming Practices
  • Watch Ashish's lectures here, but note some updates.
  • Assignment
  • Dataset for the assignment: dsfp_ztf_feats.npy
  • Software Carpentry on Github

• The Week of April 12: Matthew: Regression and Bayesian Statistics
  • Regression: Video [login with your Caltech credentials] * slides (pdf)
  • Bayesian Statistics: Video [login with your Caltech credentials]* slides (pdf)
  • Exercises: Python notebook
  • Optional: for a frequentist perspective watch Amy Braverman's lectures here

• The Week of April 19: Ashish: Machine Learning Introduction and Supervised Learning
  • Slides
  • Jupyter notebook (GROWTH/NARIT)
  • Assignment (Jupyter notebook)
  • Data for the notebooks

  • Watch Ciro Donalek's lectures on ML Introduction and Classification, Thomas Fuch's lecture on Decision Trees and Random Forests, and David Thompson's lectures on Dimensionality Reduction here
  • Watch Umaa Rebbapragada's tutorial on Machine Learning Basics from Astroinformatics 2019, and her slides (pdf) (some of it is relevant for the following week)

• The Week of April 26: Ashish: Unsupervised Learning
  • Initial slides from last week (but see also slides by others below)
  • Jupyter notebook (LSSDS/NARIT) [This covers additional methods]
  • Assignment (Jupyter notebook)
  • Data for the notebooks

  • Watch Ciro Donalek's lectures on Clustering here
  • Kiri Wagstaff's lecture "Detecting the Unexpected: An Introduction to Anomaly Detection Methods", from the KISS short course, May 2019, and her slides (pdf)
  • Watch Dalya Baron's lectures on unsupervised learning, from the IAC XXX Winter School "Big Data Analysis in Astronomy", Nov. 2018

• The Week of May 3: Dima: Databases
  • Watch Matthew's lectures here
  • Dima's NoSQL slides
  • Exercises (Jupyter notebook)

• The Week of May 10: Matthew: Time Series
  • Watch Matthew's tutorial on Time Series Analysis from Astroinformatics 2019, and his slides (pdf)
  • Ashish's slides from 2019, and a Jupyter Notebook (Melissa Hayes-Gehrke, GROWTH)
  • Exercises (Python notebook)

• The Week of May 17: Dima (Deep learning)
  • Dima's tutorial from Astroinformatics 2019, and the corresponding Python notebook
  • Ashish's talk from Astroinformatics 2019, and his slides (pdf)
  • Watch Marc Huertas-Company's lectures on Deep Learning, from the IAC XXX Winter School "Big Data Analysis in Astronomy", Nov. 2018
  • For a more in-depth coverage:
    • Andrew Ng's Deep Learning Specialization series of classes on Coursera
    • Laurence Moroney's TensorFlow in Practice Specialization series of classes on Coursera
    • These and other classes are also accessible through deeplearning.ai
  • Assignment (Python notebook)

• The Week of May 24: George/Santiago: Visualization
  • Watch Santiago's and Scott Davidoff's lectures here
  • Santiago's new lecture on 3D visualization and VR video (note: you have to log in with your Caltech credentials; may take a while to load), and his slides (pdf)
  • Watch George's lecture on data visualization from the IAC XXX Winter School "Big Data Analysis in Astronomy", Nov. 2018, and his slides (pdf)
  • Assignment (pdf)

• The Week of May 31: TBD

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  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.

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

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