Cover -- Title -- Copyright -- Contents -- Preface -- I Introduction -- 1 About the Book and Supporting Material -- 1.1 What Do Data Mining, Machine Learning, and Knowledge Discovery Mean? -- 1.2 What is This Book About? -- 1.3 An Incomplete Survey of the Relevant Literature -- 1.4 Introduction to the Python Language and the Git Code Management Tool -- 1.5 Description of Surveys and Data Sets Used in Examples -- 1.6 Plotting and Visualizing the Data in This Book -- 1.7 How to Efficiently Use This Book -- References -- 2 Fast Computation on Massive Data Sets -- 2.1 Data Types and Data Management Systems -- 2.2 Analysis of Algorithmic Efficiency -- 2.3 Seven Types of Computational Problem -- 2.4 Seven Strategies for Speeding Things Up -- 2.5 Case Studies: Speedup Strategies in Practice -- References -- II Statistical Frameworks and Exploratory Data Analysis -- 3 Probability and Statistical Distributions -- 3.1 Brief Overview of Probability and Random Variables -- 3.2 Descriptive Statistics -- 3.3 Common Univariate Distribution Functions -- 3.4 The Central Limit Theorem -- 3.5 Bivariate and Multivariate Distribution Functions -- 3.6 Correlation Coefficients -- 3.7 Random Number Generation for Arbitrary Distributions -- References -- 4 Classical Statistical Inference -- 4.1 Classical vs. Bayesian Statistical Inference -- 4.2 Maximum Likelihood Estimation (MLE) -- 4.3 The goodness of Fit and Model Selection -- 4.4 ML Applied to Gaussian Mixtures: The Expectation Maximization Algorithm -- 4.5 Confidence Estimates: the Bootstrap and the Jackknife -- 4.6 Hypothesis Testing -- 4.7 Comparison of Distributions -- 4.8 Nonparametric Modeling and Histograms -- 4.9 Selection Effects and Luminosity Function Estimation -- 4.10 Summary -- References -- 5 Bayesian Statistical Inference -- 5.1 Introduction to the Bayesian Method -- 5.2 Bayesian Priors.

5.3 Bayesian Parameter Uncertainty Quantification -- 5.4 Bayesian Model Selection -- 5.5 Nonuniform Priors: Eddington, Malmquist, and Lutz-Kelker Biases -- 5.6 Simple Examples of Bayesian Analysis: Parameter Estimation -- 5.7 Simple Examples of Bayesian Analysis: Model Selection -- 5.8 Numerical Methods for Complex Problems (MCMC) -- 5.9 Summary of Pros and Cons for Classical and Bayesian methods -- References -- III Data Mining and Machine Learning -- 6 Searching for Structure in Point Data -- 6.1 Nonparametric Density Estimation -- 6.2 Nearest-Neighbor Density Estimation -- 6.3 Parametric Density Estimation -- 6.4 Finding Clusters in Data -- 6.5 Correlation Functions -- 6.6 Which Density Estimation and Clustering Algorithms Should I Use? -- References -- 7 Dimensionality and Its Reduction -- 7.1 The Curse of Dimensionality -- 7.2 The Data Sets Used in This Chapter -- 7.3 Principal Component Analysis -- 7.4 Nonnegative Matrix Factorization -- 7.5 Manifold Learning -- 7.6 Independent Component Analysis and Projection Pursuit -- 7.7 Which Dimensionality Reduction Technique Should I Use? -- References -- 8 Regression and Model Fitting -- 8.1 Formulation of the Regression Problem -- 8.2 Regression for Linear Models -- 8.3 Regularization and Penalizing the Likelihood -- 8.4 Principal Component Regression -- 8.5 Kernel Regression -- 8.6 Locally Linear Regression -- 8.7 Nonlinear Regression -- 8.8 Uncertainties in the Data -- 8.9 Regression that is Robust to Outliers -- 8.10 Gaussian Process Regression -- 8.11 Overfitting, Underfitting, and Cross-Validation -- 8.12 Which Regression Method Should I Use? -- References -- 9 Classification -- 9.1 Data Sets Used in This Chapter -- 9.2 Assigning Categories: Classification -- 9.3 Generative Classification -- 9.4 K-Nearest-Neighbor Classifier -- 9.5 Discriminative Classification -- 9.6 Support Vector Machines.

9.7 Decision Trees -- 9.8 Evaluating Classifiers: ROC Curves -- 9.9 Which Classifier Should I Use? -- References -- 10 Time Series Analysis -- 10.1 Main Concepts for Time Series Analysis -- 10.2 Modeling Toolkit for Time Series Analysis -- 10.3 Analysis of Periodic Time Series -- 10.4 Temporally Localized Signals -- 10.5 Analysis of Stochastic Processes -- 10.6 Which Method Should I Use for Time Series Analysis? -- References -- IV Appendices -- A An Introduction to Scientific Computing with Python -- A.1 A Brief History of Python -- A.2 The SciPy Universe -- A.3 Getting Started with Python -- A.4 IPython: The Basics of Interactive Computing -- A.5 Introduction to NumPy -- A.6 Visualization with Matplotlib -- A.7 Overview of Useful NumPy/SciPy Modules -- A.8 Efficient Coding with Python and NumPy -- A.9 Wrapping Existing Code in Python -- A.10 Other Resources -- B AstroML:Machine Learning for Astronomy -- B.1 Introduction -- B.2 Dependencies -- B.3 Tools Included in AstroML v0.1 -- C Astronomical Flux Measurements andMagnitudes -- C.1 The Definition of the Specific Flux -- C.2 Wavelength Window Function for Astronomical Measurements -- C.3 The Astronomical Magnitude Systems -- D SQL Query for Downloading SDSS Data -- E Approximating the Fourier Transform with the FFT -- References -- Visual Figure Index -- Index.