ykwang1 / astr-598a-win22

A598a: Statistics and Machine Learning

ASTR 598, Winter 2022, University of Washington:

Machine Learning in Astronomy

Andy Connolly (@connolly), Stephen Portillo (@stephenportillo)

This repository contains ASTR 598 class materials.

Location

• When: MW, 2:00pm-3:20pm
• Where: PAB B305

Class Materials

• Syllabus and course description
• Lectures: Notebooks
• Canvas.
• JupyterHub: JupyterHub

Reference textbook

Ivezić, Connolly, VanderPlas & Gray: Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data; first or second edition.

Class Description

This course will introduce graduate students to common statistical and machine learning methods used in astronomy and other physical sciences. While it will include theoretical and methoodological backgrounds for the techniques the focus will be on the application of machine learning to astrophysical problems. To accomplish this we will utilize modern astronomical datasets. Practical data analysis will be done using python tools, such as astroML module (see www.astroML.org), scikit-learn, and tensor flow/pytorch. While focused on astronomy, this course should be useful to all graduate students interested in data analysis in physical sciences and engineering. The lectures will be aimed at graduate students and the main discussion topics will be based on selected topics from Chapters 6-10, in the reference textbook.

The goal of this course is to give you the tools necessary to understand and analyze rich datasets, such as thoose from SDSS to LSST. A prerequisite for this course is ASTR 598A Introduction to Astrostatistics and Data-Intensive Astronomy (offered in the Autumn quarter).