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E-raamat: Python for Probability, Statistics, and Machine Learning

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  • Formaat: EPUB+DRM
  • Ilmumisaeg: 16-Mar-2016
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319307176
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Python for Probability, Statistics, and Machine LearningSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 16-Mar-2016
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319307176
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This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Detailed proofs for certain important results are also provided. Modern Python modules like Pandas, Sympy, Scikit-learn, Tensorflow, and Keras are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This updated edition now includes the Fisher Exact Test and the Mann-Whitney-Wilcoxon Test. A new section on survival analysis has been included as well as substantial development of Generalized Linear Models. The new deep learning section for image processing includes an in-depth discussion of gradient descent methods that underpin all deep learning algorithms. As with the prior edition, there are new and updated *Programming Tips* that the illustrate effective Python modules and methods for scientific programming and machine learning. There are 445 run-able code blocks with corresponding outputs that have been tested for accuracy. Over 158 graphical visualizations (almost all generated using Python) illustrate the concepts that are developed both in code and in mathematics. We also discuss and use key Python modules such as Numpy, Scikit-learn, Sympy, Scipy, Lifelines, CvxPy, Theano, Matplotlib, Pandas, Tensorflow, Statsmodels, and Keras. This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming.

Arvustused

The purpose of this book is to introduce scientific Python to those who have a prior knowledge of probability and statistics as well as basic Python. this is a very valuable reference for those wishing to use these methods in a Python environment. I would strongly recommend this book for the intended audience or as a reference work. All in all, I strongly recommend this book for those who want to use Python in this area. (David E. Booth, Technometrics, Vol. 59 (2), April, 2017)

1 Getting Started with Scientific Python
1(34)
1.1 Installation and Setup
3(1)
1.2 Numpy
4(9)
1.2.1 Numpy Arrays and Memory
6(3)
1.2.2 Numpy Matrices
9(1)
1.2.3 Numpy Broadcasting
10(2)
1.2.4 Numpy Masked Arrays
12(1)
1.2.5 Numpy Optimizations and Prospectus
12(1)
1.3 Matplotlib
13(3)
1.3.1 Alternatives to Matplotlib
15(1)
1.3.2 Extensions to Matplotlib
16(1)
1.4 IPython
16(4)
1.4.1 IPython Notebook
18(2)
1.5 Scipy
20(1)
1.6 Pandas
21(4)
1.6.1 Series
21(2)
1.6.2 Dataframe
23(2)
1.7 Sympy
25(2)
1.8 Interfacing with Compiled Libraries
27(1)
1.9 Integrated Development Environments
28(1)
1.10 Quick Guide to Performance and Parallel Programming
29(3)
1.11 Other Resources
32(3)
References
32(3)
2 Probability
35(66)
2.1 Introduction
35(15)
2.1.1 Understanding Probability Density
36(1)
2.1.2 Random Variables
37(5)
2.1.3 Continuous Random Variables
42(3)
2.1.4 Transformation of Variables Beyond Calculus
45(2)
2.1.5 Independent Random Variables
47(2)
2.1.6 Classic Broken Rod Example
49(1)
2.2 Projection Methods
50(4)
2.2.1 Weighted Distance
53(1)
2.3 Conditional Expectation as Projection
54(6)
2.3.1 Appendix
60(1)
2.4 Conditional Expectation and Mean Squared Error
60(4)
2.5 Worked Examples of Conditional Expectation and Mean Square Error Optimization
64(14)
2.5.1 Example
64(4)
2.5.2 Example
68(2)
2.5.3 Example
70(3)
2.5.4 Example
73(1)
2.5.5 Example
74(3)
2.5.6 Example
77(1)
2.6 Information Entropy
78(5)
2.6.1 Information Theory Concepts
79(2)
2.6.2 Properties of Information Entropy
81(1)
2.6.3 Kullback-Leibler Divergence
82(1)
2.7 Moment Generating Functions
83(4)
2.8 Monte Carlo Sampling Methods
87(8)
2.8.1 Inverse CDF Method for Discrete Variables
88(2)
2.8.2 Inverse CDF Method for Continuous Variables
90(2)
2.8.3 Rejection Method
92(3)
2.9 Useful Inequalities
95(6)
2.9.1 Markov's Inequality
96(1)
2.9.2 Chebyshev's Inequality
97(1)
2.9.3 Hoeffding's Inequality
98(1)
References
99(2)
3 Statistics
101(96)
3.1 Introduction
101(1)
3.2 Python Modules for Statistics
102(2)
3.2.1 Scipy Statistics Module
102(1)
3.2.2 Sympy Statistics Module
103(1)
3.2.3 Other Python Modules for Statistics
104(1)
3.3 Types of Convergence
104(7)
3.3.1 Almost Sure Convergence
105(2)
3.3.2 Convergence in Probability
107(2)
3.3.3 Convergence in Distribution
109(1)
3.3.4 Limit Theorems
110(1)
3.4 Estimation Using Maximum Likelihood
111(14)
3.4.1 Setting Up the Coin Flipping Experiment
113(10)
3.4.2 Delta Method
123(2)
3.5 Hypothesis Testing and P-Values
125(16)
3.5.1 Back to the Coin Flipping Example
126(4)
3.5.2 Receiver Operating Characteristic
130(2)
3.5.3 P-Values
132(1)
3.5.4 Test Statistics
133(7)
3.5.5 Testing Multiple Hypotheses
140(1)
3.6 Confidence Intervals
141(3)
3.7 Linear Regression
144(14)
3.7.1 Extensions to Multiple Covariates
154(4)
3.8 Maximum A-Posteriori
158(6)
3.9 Robust Statistics
164(7)
3.10 Bootstrapping
171(5)
3.10.1 Parametric Bootstrap
175(1)
3.11 Gauss Markov
176(4)
3.12 Nonparametric Methods
180(17)
3.12.1 Kernel Density Estimation
180(3)
3.12.2 Kernel Smoothing
183(5)
3.12.3 Nonparametric Regression Estimators
188(1)
3.12.4 Nearest Neighbors Regression
189(4)
3.12.5 Kernel Regression
193(1)
3.12.6 Curse of Dimensionality
194(2)
References
196(1)
4 Machine Learning
197(78)
4.1 Introduction
197(1)
4.2 Python Machine Learning Modules
197(4)
4.3 Theory of Learning
201(24)
4.3.1 Introduction to Theory of Machine Learning
203(4)
4.3.2 Theory of Generalization
207(2)
4.3.3 Worked Example for Generalization/Approximation Complexity
209(6)
4.3.4 Cross-Validation
215(4)
4.3.5 Bias and Variance
219(3)
4.3.6 Learning Noise
222(3)
4.4 Decision Trees
225(9)
4.4.1 Random Forests
232(2)
4.5 Logistic Regression
234(6)
4.5.1 Generalized Linear Models
239(1)
4.6 Regularization
240(10)
4.6.1 Ridge Regression
244(4)
4.6.2 Lasso
248(2)
4.7 Support Vector Machines
250(6)
4.7.1 Kernel Tricks
253(3)
4.8 Dimensionality Reduction
256(8)
4.8.1 Independent Component Analysis
260(4)
4.9 Clustering
264(4)
4.10 Ensemble Methods
268(7)
4.10.1 Bagging
268(3)
4.10.2 Boosting
271(2)
References
273(2)
Index 275
Dr. José Unpingco completed his PhD from the University of California, San Diego in 1998 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 multiple machine learning technologies. He was the onsite technical director for large-scale Signal and Image Processing for the Department of Defense (DoD) where he also spearheaded the DoD-wide adoption of scientific Python. As the primary scientific Python instructor for the DoD, he has taught Python to over 600 scientists and engineers. Dr. Unpingco is currently the Technical Director for Data Science for a non-profit Medical Research Organization in San Diego, California.
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