Python machine learning

A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges.The goal of data

Mixture analysis is very active research topic in statistics and machine learning. It is a good timing for a Handbook to present a broad overview of the methods and applications, suitable for graduate

This book introduces the challenges of robotic tactile perception and task understanding, and describes an advanced approach based on machine learning and sparse coding techniques. Further, a set of s

Become a machine learning pro! Google TensorFlow has become the darling of financial firms and research organizations, but the technology can be intimidating and the learning curve is steep. Luc

本書以新的Spark2.0爲技術基礎，重點講解了如何構建機器學習系統以及如何實現機器學習流程的標準化，這兩點都是目前同類書中沒有的。第1～7章從概念、架構、算法等角度介紹了機器學習的基本概念；第8～12章以實例爲主，詳細講解了機器學習流程標準化涉及的關鍵技術；第13章主要以在綫數據或流式數據爲主介紹了流式計算框架SparkStreaming；第14章重點講解了深度學習的框架TensorFlowOn

The book ‘Data Intensive Computing Applications for Big Data’ discusses the technical concepts of big data, data intensive computing through machine learning, soft computing and parallel computing par

Predictive Analytics has revolutionised marketing practice. It involves using many techniques from data mining, statistics, modelling, machine learning and artificial intelligence, to analyse current

An Introduction to Machine Learning in Finance, With Mathematical Background, Data Visualization, and R Nonparametric function estimation is an important part of machine learning, which is becoming in

The subjects of statistics and probability are interrelated and are generally studied together. They can be applied in a number of fields such as finance, machine learning, game theory, etc. The book

This book is published open access under a CC BY 4.0 license.It covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cl

This book provides over 1,000 review questions and answers for all types of mechanical engineering exams. It covers all the aspects of mechanical engineering topics including thermodynamics, machine d

Get started with Julia for engineering and numerical computing, especially data science, machine learning, and scientific computing applications. This book explains how Julia provides the functionalit

Machine Learning: A Constraint-Based Approach provides readers with a refreshing look at the basic models and algorithms of machine learning, with an emphasis on current topics of interest that includ

This comprehensive text/reference presents a broad review of diverse domain adaptation (DA) methods for machine learning, with a focus on solutions for visual applications. The book collects together

This book is intended for use in advanced graduate courses in statistics / machine learning, as well as for all experimental neuroscientists seeking to understand statistical methods at a deeper level

A straightforward, non-technical guide to the next major marketing tool Artificial Intelligence for Marketing presents a tightly-focused introduction to machine learning, written specifically for mark

Copyright 2016 Packt Publishing. First published in the English language under the title “Mastering Scala Machine Learning”.All rights reserved.Chinese simplified language edition published by China M

Molecular biologists are performing increasingly large and complicated experiments, but often have little background in data analysis. The book is devoted to teaching the statistical and computational

Machine learning algorithms and artificial intelligence influence many aspects of life today. This report identifies some of their shortcomings and associated policy risks and examines some approaches

This book introduces a paradigm of reverse hypothesis machines (RHM), focusing on knowledge innovation and machine learning. Knowledge- innovation-based learning is constrained by large volumes of dat

This unique text/reference provides a detailed overview of the latest advances in machine learning and computer vision related to visual attributes, highlighting how this emerging field intersects wit

Computer scientists describe the current understanding and practice of machine learning, both supervised and unsupervised, in a range of fields. They cover mobile vision for a plant biometric syste

Learn the fundamental aspects of the business statistics, data mining, and machine learning techniques required to understand the huge amount of data generated by your organization. This book explains

Master the basics of print interpretation! PRINT READING FOR MACHINISTS, Sixth Edition, is an ideal resource for machine trades students and apprentices alike who want to gain the knowledge and skills

Greatly expanded, this new edition requires only an elementary background in discrete mathematics and offers a comprehensive introduction to the role of randomization and probabilistic techniques in modern computer science. Newly added chapters and sections cover topics including normal distributions, sample complexity, VC dimension, Rademacher complexity, power laws and related distributions, cuckoo hashing, and the Lovasz Local Lemma. Material relevant to machine learning and big data analysis enables students to learn modern techniques and applications. Among the many new exercises and examples are programming-related exercises that provide students with excellent training in solving relevant problems. This book provides an indispensable teaching tool to accompany a one- or two-semester course for advanced undergraduate students in computer science and applied mathematics.

This book provides comprehensive coverage of the field of outlier analysis from a computer science point of view. It integrates methods from data mining, machine learning, and statistics within the co

There’s growing interest in learning how to analyze streaming data in large-scale systems such as web traffic, financial transactions, machine logs, industrial sensors, and many others. But analyzing

This book covers aspects of human re-identification problems related to computer vision and machine learning. Working from a practical perspective, it introduces novel algorithms and designs for human

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction

This book lays out a path leading from the linguistic and cognitive basics, to classical rule-based and machine learning algorithms, to today’s state-of-the-art approaches, which use advanced empirica

With this comprehensive and accessible introduction to the field, you will gain all the skills and knowledge needed to work with current and future audio, speech, and hearing processing technologies. Topics covered include mobile telephony, human-computer interfacing through speech, medical applications of speech and hearing technology, electronic music, audio compression and reproduction, big data audio systems and the analysis of sounds in the environment. All of this is supported by numerous practical illustrations, exercises, and hands-on MATLAB® examples on topics as diverse as psychoacoustics (including some auditory illusions), voice changers, speech compression, signal analysis and visualisation, stereo processing, low-frequency ultrasonic scanning, and machine learning techniques for big data. With its pragmatic and application driven focus, and concise explanations, this is an essential resource for anyone who wants to rapidly gain a practical understanding of speech and audi

Social media is an invaluable source of time-critical information during a crisis. However, emergency response and humanitarian relief organizations that would like to use this information struggle with an avalanche of social media messages that exceeds the human capacity to process. Emergency managers, decision makers, and affected communities can make sense of social media through a combination of machine computation and human compassion - expressed by thousands of digital volunteers who publish, process, and summarize potentially life-saving information. This book brings together computational methods from many disciplines: natural language processing, semantic technologies, data mining, machine learning, network analysis, human-computer interaction, and information visualization, focusing on methods that are commonly used for processing social media messages under time-critical constraints, and offering more than 500 references to in-depth information.

This textbook provides concise coverage of the basics of linear and integer programming which, with megatrends toward optimization, machine learning, big data, etc., are becoming fundamental toolkits

Reproducing kernel Hilbert spaces have developed into an important tool in many areas, especially statistics and machine learning, and they play a valuable role in complex analysis, probability, group representation theory, and the theory of integral operators. This unique text offers a unified overview of the topic, providing detailed examples of applications, as well as covering the fundamental underlying theory, including chapters on interpolation and approximation, Cholesky and Schur operations on kernels, and vector-valued spaces. Self-contained and accessibly written, with exercises at the end of each chapter, this unrivalled treatment of the topic serves as an ideal introduction for graduate students across mathematics, computer science, and engineering, as well as a useful reference for researchers working in functional analysis or its applications.

Utilising both key mathematical tools and state-of-the-art research results, this text explores the principles underpinning large-scale information processing over networks and examines the crucial interaction between big data and its associated communication, social and biological networks. Written by experts in the diverse fields of machine learning, optimisation, statistics, signal processing, networking, communications, sociology and biology, this book employs two complementary approaches: first analysing how the underlying network constrains the upper-layer of collaborative big data processing, and second, examining how big data processing may boost performance in various networks. Unifying the broad scope of the book is the rigorous mathematical treatment of the subjects, which is enriched by in-depth discussion of future directions and numerous open-ended problems that conclude each chapter. Readers will be able to master the fundamental principles for dealing with big data over

Till Albert presents a machine learning based approach to harnessing information contained in big data from different media sources such as patents, scientific publications, or the internet. He shows

If you’re a data scientist ready to tackle statistical and machine learning techniques across large data sets, this practical guide provides a solid introduction to the world of clustered computing an

In the last decade, there have been an increasing convergence of interest and methods between theoretical physics and fields as diverse as probability, machine learning, optimization and compressed se

This edited volume is devoted to Big Data Analysis from a Machine Learning standpoint as presented by some of the most eminent researchers in this area.It demonstrates that Big Data Analysis opens up