Combinatorial Machine Learning

Geometric and topological inference deals with the retrieval of information about a geometric object using only a finite set of possibly noisy sample points. It has connections to manifold learning and provides the mathematical and algorithmic foundations of the rapidly evolving field of topological data analysis. Building on a rigorous treatment of simplicial complexes and distance functions, this self-contained book covers key aspects of the field, from data representation and combinatorial questions to manifold reconstruction and persistent homology. It can serve as a textbook for graduate students or researchers in mathematics, computer science and engineering interested in a geometric approach to data science.

This groundbreaking textbook combines straightforward explanations with a wealth of practical examples to offer an innovative approach to teaching linear algebra. Requiring no prior knowledge of the subject, it covers the aspects of linear algebra - vectors, matrices, and least squares - that are needed for engineering applications, discussing examples across data science, machine learning and artificial intelligence, signal and image processing, tomography, navigation, control, and finance. The numerous practical exercises throughout allow students to test their understanding and translate their knowledge into solving real-world problems, with lecture slides, additional computational exercises in Julia and MATLAB®, and data sets accompanying the book online. Suitable for both one-semester and one-quarter courses, as well as self-study, this self-contained text provides beginning students with the foundation they need to progress to more advanced study.

Malware Data Science explains how to identify, analyze, and classify large-scale malware using machine learning and data visualization.Security has become a "big data" problem. The growth rate of malw

This book introduces the concepts, applications and development of data science in the telecommunications industry by focusing on advanced machine learning and data mining methodologies in the wireles

This book proposes neural networks algorithms and advanced machine learning techniques for processing nonlinear dynamic signals such as audio, speech, financial signals, feedback loops, waveform gener

This book presents conjectural advances in big data analysis, machine learning and computational intelligence, as well as their potential applications in scientific computing. It discusses major issue

This Level 1 guided reader series explores how road rollers are used on construction sites. Students will develop word recognition and reading skills while learning about exciting construction machine

近年來，機器學習領域受到越來越多的關注，相關的機器學習演算法開始成為熱點。 本書專門介紹了有關機器學習的內容，全書共分3部分：第1部分是資料科學和Microsoft Azure Machine Learning導論，介紹了資料科學和Microsoft Azure Machine Learning的基本知識以及需要用到的語言的基本知識；第二部分是統計學和機器學習演算法，系統地講解了統計學和機器學習的

This book is a delight for academics, researchers and professionals working in evolutionary and swarm computing, computational intelligence, machine learning and engineering design, as well as search

本書探索了一系列廣泛應用的機器學習技術，用實際例子揭示了常見數據中隱藏的處理技巧和竅門。本書還探討了機器學習的前沿進展，提供了一些關於深度學習和強化學習的範例和指導原則，完美演繹了實操過程及算法原理。本書的目標讀者為那些想瞭解機器學習實踐通過機器學習技術解決現實應用的數據科學家。

Machine Learning Techniques for Space Weather provides a thorough and accessible presentation of machine learning techniques that can be employed by space weather professionals. Additionally, it prese

本書講解的是在R語言平臺上使用大數據技術構建可擴展機器學習模型的新技術成果。它全面展示了如何採用機器學習算法在原始數據的基礎上構建機器學習模型。本書還能讓那些希望利用ApacheHadoop、Hive、Pig和Spark來實現可擴展機器學習模型的讀者從中受益。

Machine learning and computerized data analysis is incredibly powerful, but humans still excel at "getting a sense of things"—the ability to recognize ill-defined or novel patterns or anomalies.

This open access book describes the results of natural language processing and machine learning methods applied to clinical text from electronic patient records. It is divided into twelve chapters. Ch

Originally developed to support video games, graphics processor units (GPUs) are now increasingly used for general-purpose (non-graphics) applications ranging from machine learning to mining of crypto

Accelerated Path to Cures provides a transformative perspective on the power of combining advanced computational technologies, modeling, bioinformatics and machine learning approaches with nonclinical

本書採用理論與實踐相結合的方式，在簡明扼要地闡明機器學習原理的基礎上，通過大量實例介紹了不同場景下機器學習算法在scikit-learn中的實現及應用。書中還有大量的代碼示例及圖例，便於讀者理解和學習並實際上手操作。另一方面，書中還有很多的延伸閱讀指導，方便讀者系統性地瞭解機器學習領域的現有技術及其發展狀態。

本書對機器學習的關鍵知識點進行了全面講解，幫助讀者順利完成從理論到實踐的過渡。書中首先介紹用於描述機器學習算法的統計與概率的知識，接著詳細分析機器學習技術的兩類主要方法――生成方法和判別方法，後深入研究了如何使機器學習算法在實際應用中發揮更大的作用。本書提供程序源代碼，便於讀者進行數據分析實踐。本書適合高等院校計算機、統計等專業的研究生和高年級本科生閱讀，同時也適合相關領域的技術人員參考。

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

Modern NLP techniques based on machine learning radically improve the ability of software to recognize patterns, use context to infer meaning, and accurately discern intent from poorly-structured text

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

Quantum annealing is a new-generation tool of information technology, which helps in solving combinatorial optimization problems with high precision, based on the concepts of quantum statistical physics. Detailed discussion on quantum spin glasses and its application in solving combinatorial optimization problems is required for better understanding of quantum annealing concepts. Fulfilling this requirement, the book highlights recent development in quantum spin glasses including Nishimori line, replica method and quantum annealing methods along with the essential principles. Separate chapters on simulated annealing, quantum dynamics and classical spin models are provided for enhanced learning. Important topics including adiabatic quantum computers and quenching dynamics are discussed in detail. This text will be useful for students of quantum computation, quantum information, statistical physics and computer science.