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A. An Introduction to Scientific Computing with Python
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Chapters in this book
- Frontmatter i
- Contents v
- Preface ix
-
I. Introduction
- 1. About the Book and Supporting Material 1
- 2. Fast Computation on Massive Data Sets 41
-
II. Statistical Frameworks and Exploratory Data Analysis
- 3. Probability and Statistical Distributions 65
- 4. Classical Statistical Inference 115
- 5. Bayesian Statistical Inference 165
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III. Data Mining and Machine Learning
- 6. Searching for Structure in Point Data 243
- 7. Dimensionality and Its Reduction 281
- 8. Regression and Model Fitting 311
- 9. Classification 353
- 10. Time Series Analysis 399
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IV. Appendices
- A. An Introduction to Scientific Computing with Python 467
- B. AstroML:Machine Learning for Astronomy 505
- C. Astronomical Flux Measurements and Magnitudes 509
- D. SQL Query for Downloading SDSS Data 513
- E. Approximating the Fourier Transform with the FFT 515
- Visual Figure Index 521
- Index 529
Chapters in this book
- Frontmatter i
- Contents v
- Preface ix
-
I. Introduction
- 1. About the Book and Supporting Material 1
- 2. Fast Computation on Massive Data Sets 41
-
II. Statistical Frameworks and Exploratory Data Analysis
- 3. Probability and Statistical Distributions 65
- 4. Classical Statistical Inference 115
- 5. Bayesian Statistical Inference 165
-
III. Data Mining and Machine Learning
- 6. Searching for Structure in Point Data 243
- 7. Dimensionality and Its Reduction 281
- 8. Regression and Model Fitting 311
- 9. Classification 353
- 10. Time Series Analysis 399
-
IV. Appendices
- A. An Introduction to Scientific Computing with Python 467
- B. AstroML:Machine Learning for Astronomy 505
- C. Astronomical Flux Measurements and Magnitudes 509
- D. SQL Query for Downloading SDSS Data 513
- E. Approximating the Fourier Transform with the FFT 515
- Visual Figure Index 521
- Index 529