Author 
Unpingco, JosA, http://id.loc.gov/vocabulary/relators/aut.

Subject 
Telecommunication.


Computer science.


Engineering mathematics.


Statistics.


Data mining.

Description 
1 online resource (XIV, 384 pages 164 illustrations, 37 illustrations in color. :) : online resource 
Edition 
Second edition 2019. 
Contents 
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 PValues  Confidence Intervals  Linear Regression  Maximum APosteriori  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. 
Summary 
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, Scikitlearn, Tensorflow, and Keras are applied to simulate and visualize important machine learning concepts like the bias/variance tradeoff, crossvalidation, 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 MannWhitneyWilcoxon 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 indepth 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 runable 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, Scikitlearn, Sympy, Scipy, Lifelines, CvxPy, Theano, Matplotlib, Pandas, Tensorflow, Statsmodels, and Keras. This book is suitable for anyone with an undergraduatelevel exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming. 
Bibliography Note 
Includes bibliographical references and index. 
ISBN 
9783030185459 

3030185451 

9783030185459 

3030185443 

9783030185442 
ISBN/ISSN 
10.1007/978303018 
OCLC # 
1110991970 
Additional Format 
Printed edition: 9783030185442. 

Printed edition: 9783030185466. 

Printed edition: 9783030185473. 
