Slides from Astroinformatics Summer School 2022

The School offers advanced lessons on applying data-driven models to address challenges of modern astronomy research, such as incorporating machine learning, mining large astronomical surveys, harnessing parallel computing architectures, Bayesian computation, and integrating these with domain-specific knowledge to achieve more than can be done with either traditional methods or machine learning individually.

Lectures will be presented by experienced instructors in astroinformatics. Lab tutorials in the form of computational notebooks will reinforce the learning experience, encouraging participants to exercise the methods with astronomical datasets illustrating realistic challenges faced in contemporary research.

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Work Title Slides from Astroinformatics Summer School 2022
Access
Open Access
Creators
  1. Eric Ford
  2. Joel Leja
  3. Hyung Suk Tak
  4. Murali Haran
  5. Chuck Pavloski
  6. Justin Matthew Petucci
  7. V. Ashley Villar
  8. Tamas Budavari
  9. Rodrigo Luger
  10. Chris Rackauckas
  11. Jeffrey Regier
Keyword
  1. Astroinformatics
  2. Center for Astrostatistics
License CC BY-SA 4.0 (Attribution-ShareAlike)
Work Type Presentation
Publisher
  1. Penn State Center for Astrostatistics
Publication Date June 2022
Subject
  1. Astronomy
  2. Astrophysics
  3. Statistics
  4. Machine Learning
  5. Data Sciences
Language
  1. English
DOI doi:10.26207/bkax-d915
Related URLs
  1. https://github.com/Astroinformatics/SummerSchool2022
  2. https://sites.psu.edu/astrostatistics/astroinfo-su22-program/
Deposited August 19, 2022

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Version 1
published

  • Created

    August 19, 2022 11:54 by ebf11

  • Updated

    August 19, 2022 11:54 by [unknown user]

  • Updated Work Title, Keyword, Subject, and 2 more Show Changes

    August 19, 2022 11:56 by ebf11

    Work Title
    • Introduction to Philosophy of Data-Driven Models & Machine Learning
    • Slides from Astroinformatics Summer School 2022
    Keyword
    • Astroinformatics, Center for Astrostatistics
    Subject
    • Astronomy, Astrophysics, Statistics, Machine Learning, Data Sciences
    Language
    • English
    Publisher
    • Penn State Center for Astrostatistics
  • Added Creator Eric Ford

    August 19, 2022 12:00 by ebf11

  • Added Creator Joel Leja

    August 19, 2022 12:00 by ebf11

  • Added Creator Hyung Suk Tak

    August 19, 2022 12:00 by ebf11

  • Added Creator Murali Haran

    August 19, 2022 12:00 by ebf11

  • Added Creator Chuck Pavloski

    August 19, 2022 12:00 by ebf11

  • Added Creator Justin Matthew Petucci

    August 19, 2022 12:00 by ebf11

  • Added Creator V. Ashley Villar

    August 19, 2022 12:00 by ebf11

  • Added Creator Tamas Budavari

    August 19, 2022 12:00 by ebf11

  • Added Creator Rodrigo Luger

    August 19, 2022 12:00 by ebf11

  • Added Creator Chris Rackauckas

    August 19, 2022 12:00 by ebf11

  • Added Creator Jeffrey Regier

    August 19, 2022 12:00 by ebf11

  • Added Lab 4 Instructions SQL.pdf

    August 19, 2022 12:02 by ebf11

  • Added Lesson 14 Putting the Peice Together Ford.pdf

    August 19, 2022 12:02 by ebf11

  • Added Lesson 13 High-Performance Computing Resources Pavloski.pdf

    August 19, 2022 12:02 by ebf11

  • Added Lesson 12 High-Performance Computing Concepts.pdf

    August 19, 2022 12:02 by ebf11

  • Added Lesson 11 Scientific Machine Learning Rackauckas (1).pdf

    August 19, 2022 12:02 by ebf11

  • Added Lesson 10 Variational Inference Regeir.pdf

    August 19, 2022 12:02 by ebf11

  • Added Lesson 9 Neural Networks Tak.pdf

    August 19, 2022 12:02 by ebf11

  • Added Lesson 8 Bayesian Computation Haran.pdf

    August 19, 2022 12:02 by ebf11

  • Added Lesson 7 Hierarchical Bayesian Modeling Leja.pdf

    August 19, 2022 12:02 by ebf11

  • Added Lesson 6 Dimensionality Reduction and Representation Learning Villar (1).pdf

    August 19, 2022 12:02 by ebf11

  • Added Lesson 5 Regularization Luger.pdf

    August 19, 2022 12:02 by ebf11

  • Added Lesson 3 Databases Budavari.pdf

    August 19, 2022 12:02 by ebf11

  • Added Lecsson 2 Classification Tak with notes.pdf

    August 19, 2022 12:02 by ebf11

  • Added Lesson 1 Regression Tak with notes.pdf

    August 19, 2022 12:02 by ebf11

  • Added Lesson 0 Philosophy of Astroinformatics Ford.pdf

    August 19, 2022 12:02 by ebf11

  • Updated Description, License Show Changes

    August 19, 2022 12:03 by ebf11

    Description
    • The School offers advanced lessons on applying data-driven models to address challenges of modern astronomy research, such as incorporating machine learning, mining large astronomical surveys, harnessing parallel computing architectures, Bayesian computation, and integrating these with domain-specific knowledge to achieve more than can be done with either traditional methods or machine learning individually.
    • Lectures will be presented by experienced instructors in astroinformatics. Lab tutorials in the form of computational notebooks will reinforce the learning experience, encouraging participants to exercise the methods with astronomical datasets illustrating realistic challenges faced in contemporary research.
    License
    • https://creativecommons.org/licenses/by-sa/4.0/
  • Published

    August 19, 2022 12:03 by ebf11