seanreed1111 / astr-324-s17

Materials for ASTR 324 class at the University of Washington

ASTR 324, Spring 2017, University of Washington:

Introduction to AstroStatistics and Big Data in Astronomy

Željko Ivezić and Mario Jurić

Location

• When: 2:30-3:50, Tuesday & Thursday, Spring quarter 2017
• Where: PAB A210 (Physics-Astronomy Classroom Building)

Class Materials

• Syllabus and course description
• Lectures
• Homeworks

Reference textbook

Ivezić, Connolly, VanderPlas & Gray: Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data (Princeton University Press, 2014)

Class Description

This course will introduce students to most common statistical and computer science methods used in astronomy and other physical sciences. It will combine theoretical background with examples of data analysis based on modern astronomical datasets. Practical data analysis will be done using python tools, with emphasis on astroML module (see www.astroML.org). While focused on astronomy, this course should be useful to all students interested in data analysis in physical sciences and engineering. The lectures will be aimed at undergraduate students and the main discussion topics will be based on Chapters 4 and 5, and selected topics from Chapters 6-10, from the reference textbook.

By taking this course, students will develop basic understanding of topics such as robust statistics, hypothesis testing, maximum likelihood analysis, Bayesian statistics, model parameter estimation, the goodness of fit and model selection, density estimation and clustering, unsupervised and supervised classification, dimensionality reduction, regression and time series analysis. Most of these topics will be applied in class homeworks to analysis of astronomical data.