User Guide

• 1. Introduction
  • 1.1. Philosophy
  • 1.2. Textbook
• 2. Installation of astroML
  • 2.1. Important Links
  • 2.2. Installation
  • 2.3. Dependencies
• 3. Data Sets
  • 3.1. Data Set Cache Location
  • 3.2. SDSS Data
  • 3.3. Combined Surveys
  • 3.4. Time Domain Data
  • 3.5. WMAP temperature map
• 4. Unsupervised Learning: Density Estimation
  • 4.1. Bayesian Blocks: Histograms the Right Way
  • 4.2. Extreme Deconvolution
  • 4.3. Kernel Density Estimation
  • 4.4. Nearest Neighbors Density Estimation
• 5. Unsupervised Learning: Clustering
• 6. Two-point Correlation Functions
• 7. Unsupervised Learning: Dimensionality Reduction
• 8. Supervised Learning: Regression
  • 8.1. Lets use simulated data for the examples below
  • 8.2. Simple linear regression
  • 8.3. Measurement Errors in Linear Regression
  • 8.4. Measurement errors in both dependent and independent variables
• 9. Supervised Learning: Classification
• 10. Time Series Analysis
• 11. Class reference
  • 11.1. Plotting Functions: astroML.plotting
  • 11.2. Density Estimation & Histograms: astroML.density_estimation
  • 11.3. Linear Regression & Fitting: astroML.linear_model
  • 11.4. Loading of Datasets: astroML.datasets
  • 11.5. Time Series Analysis: astroML.time_series
  • 11.6. Statistical Functions: astroML.stats
  • 11.7. Dimensionality Reduction: astroML.dimensionality
  • 11.8. Correlation Functions: astroML.correlation
  • 11.9. Filters: astroML.filters
  • 11.10. Fourier and Wavelet Transforms: astroML.fourier
  • 11.11. Luminosity Functions: astroML.lumfunc
  • 11.12. Classification: astroML.classification
  • 11.13. Resampling: astroML.resample
• 12. Other Resources

AstroML: Machine Learning and Data Mining for Astronomy 1. Introduction