Introduction and welcome

• Eric Feigelson/LOC+SOC

Introduction to Astrostatistics & R (lecture)

• Eric Feigelson

Getting started with R and CRAN part.1 (tutorial)

• Eric Feigelson

Nonparametrics & local regression (lecture+tutorial)

• Eric Feigelson

Nonparametrics & local regression (lecture+tutorial)

• Eric Feigelson

Smooth R Regression SDSS_QSO.dat, NGC****_profile.dat

Welcome Reception

Regression: Fundamental of statistical inference (lecture+tutorial)

• Eric Feigelson

Spatial point processes (lecture+tutorial)

• Eric Feigelson

Multivariate clustering & classification (lecture+tutorial)

• Eric Feigelson

Bayesian inference & computation (lecture)

• Eric Feigelson

Nondetections: Censoring & truncation (lecture+tutorial)

• Eric Feigelson

Time series analysis part.1 (lecture+tutorial)

• Eric Feigelson

Research talk: Autoregressive planet search

• Eric Feigelson

High performance computing in R (tutorial)

• Eric Feigelson

Towards good statistical practices in astronomy (lecture)

• Eric Feigelson

Astinfo 1.1 - Introduction to Data Science (lecture)

• Ashish Mahabal

Astinfo 1.2 - Programming with Python (Jup.notebook)

• Ashish Mahabal

Astinfo 1.3a - Best programming practices (lecture)

• Ashish Mahabal

Astinfo 1.3b - Classification (lecture)

Astinfo 1.4a - Supervised classification (Jup.notebook)

• Ashish Mahabal

Astinfo 1.4b - Supervised classification exercise (Jup.notebook)

Astinfo 2.1 - Unsupervised classification (Jup.notebook)

• Ashish Mahabal

Astinfo 2.2 - Unsupervised classification exercise (Jup.notebook)

• Ashish Mahabal

Astinfo 2.3 - Deep learning (lecture)

• Ashish Mahabal

Astinfo 2.4 - Deep learning (Jup.notebook)

• Ashish Mahabal

Farewell Dinner