Introduction.- Part 1 Getting Started with Scientific Python.- Installation and Setup.- Numpy.- Matplotlib.- Ipython.- Jupyter Notebook.- Scipy.- Pandas.- Sympy.- Interfacing with Compiled Libraries.- Integrated Development Environments.- Quick Guide to Performance and Parallel Programming.- Other Resources.- Part 2 Probability.- Introduction.- Projection Methods.- Conditional Expectation as Projection.- Conditional Expectation and Mean Squared Error.- Worked Examples of Conditional Expectation and Mean Square Error Optimization.- Useful Distributions.- Information Entropy.- Moment Generating Functions.- Monte Carlo Sampling Methods.- Useful Inequalities.- Part 3 Statistics.- Python Modules for Statistics.- Types of Convergence.- Estimation Using Maximum Likelihood.- Hypothesis Testing and P-Values.- Confidence Intervals.- Linear Regression.- Maximum A-Posteriori.- Robust Statistics.- Bootstrapping.- Gauss Markov.- Nonparametric Methods.- Survival Analysis.- Part 4 Machine Learning.- Introduction.- Python Machine Learning Modules.- Theory of Learning.- Decision Trees.- Boosting Trees.- Logistic Regression.- Generalized Linear Models.- Regularization.- Support Vector Machines.- Dimensionality Reduction.- Clustering.- Ensemble Methods.- Deep Learning.- Notation.- References.- Index.

�ber die Autorin bzw. den Autor:

Dr. Jos� Unpingco completed his PhD at the University of California, San Diego in 1997 and has since worked in industry as an engineer, consultant, and instructor on a wide-variety of advanced data processing and analysis topics, with deep experience in machine learning and statistics. As the onsite technical director for large-scale Signal and Image Processing for the Department of Defense (DoD), he spearheaded the DoD-wide adoption of scientific Python. He also trained over 600 scientists and engineers to effectively utilize Python for a wide range of scientific topics -- from weather modeling to antenna analysis. Dr. Unpingco is the cofounder and Senior Director for Data Science at a non-profit Medical Research Organization in San Diego, California. He also teaches programming for data analysis at the University of California, San Diego for engineering undergraduate/graduate students. He is author of Python for Signal Processing (Springer 2014) and Python for Probability, Statistics, and Machine Learning (2016)

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