ASTR 598 A: Topics in Theoretical Astrophysics

INTRODUCTION TO ASTROSTATISTICS AND DATA-INTENSIVE ASTRONOMY THIS COURSE WILL INTRODUCE YOU TO CONCEPTS, TOOLS, AND AND TECHNIQUES FROM STATISTICS AND COMPUTER SCIENC THAT ARE ESSENTIAL FOR ACCURATE AND REPRODUCIBLE ANALYSIS OF DATASETS, LARGE AND SMALL. THROUGH A SERIES O LECTURES AND HANDS-ON PROBLEMS, WE WILL LEARN ABOUT ELEMENTARY STATISTICS, MAXIMUM LIKELIHOOD METHODS, BAYESIAN PROBABILITY AND INFERENCE, MCMC METHODS, DATABASES, AND TIME SERIES ANALYSIS. PRACTICAL DATA ANALYSIS WILL BE DONE USING PYTHON, INCLUDING ASTROML, ASTROPY, ASTROQUERY AND OTHERS. THE GOAL OF THIS COURSE IS TO GIVE YOU THE BASIC SKILLS NECESSARY TO UNDERSTAND AND CORRECTLY ANALYZE RICH DATASETS, FROM KEPLER TO LSST. IT WILL ALSO GIVE YOU THE THEORETICAL PREREQUISITES NEEDED TO SUCCESSFULLY PROCEED TO ASTR 597 MACHINE LEARNING IN ASTRONOMY (TO B OFFERED IN THE WINTER QUARTER).