Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data, Updated Ed

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Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the Large Synoptic Survey Telescope. Now fully updated, it presents a wealth of practical analysis problems, evaluates the techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. Python code and sample data sets are provided for all applications described in the book. The supporting data sets have been carefully selected from contemporary astronomical surveys and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, engage with the different methods, and adapt them to their own fields of interest. An accessible textbook for students and an indispensable reference for researchers, this updated edition features new sections on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. The chapters have been revised throughout and the astroML code has been brought completely up to date. Fully revised and expanded Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets Features real-world data sets from astronomical surveys Uses a freely available Python codebase throughout Ideal for graduate students, advanced undergraduates, and working astronomers

Machine Learning with Python: Theory and Applications

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Machine Learning (ML) has become a very important area of research widely used in various industries. This compendium introduces the basic concepts, fundamental theories, essential computational techniques, codes, and applications related to ML models. With a strong foundation, one can comfortably learn related topics, methods, and algorithms. Most importantly, readers with strong fundamentals can even develop innovative and more effective machine models for his/her problems. The book is written to achieve this goal. The useful reference text benefits professionals, academics, researchers, graduate and undergraduate students in AI, ML and neural networks. Request Inspection Copy Sample Chapter(s) Chapter 1: Introduction Contents: Introduction Basics of Python Basic Mathematical Computations Statistics and Probability-based Learning Model Prediction Function and Universal Prediction Theory The Perceptrons and SVM Activation Functions and Universal Approximation Theory Automatic Differentiation and Autograd Solution Existence Theory and Optimization Techniques Loss Functions for Regression Loss Functions and Models for Classification Multiclass Classification Multilayer Perceptron (MLP) for Regression and Classification Overfitting and Regularization Convolutional Neutral Network (CNN) for Classification and Object Detection Recurrent Neural Network (RNN)and Sequence Feature Models Unsupervised Learning Techniques Reinforcement Learning (RL) Readership: Researchers, professionals, academics, undergraduate and graduate students in AI and machine learning.