learning from data - 三民網路書店

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共347筆商品，9/18頁

Probabilistic Numerics：Computation as Machine Learning

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161.

Probabilistic Numerics：Computation as Machine Learning

作者：Philipp Hennig 出版社：Cambridge Univ Pr 出版日：2022/06/30 裝訂：精裝

Probabilistic numerical computation formalises the connection between machine learning and applied mathematics. Numerical algorithms approximate intractable quantities from computable ones. They estimate integrals from evaluations of the integrand, or the path of a dynamical system described by differential equations from evaluations of the vector field. In other words, they infer a latent quantity from data. This book shows that it is thus formally possible to think of computational routines as learning machines, and to use the notion of Bayesian inference to build more flexible, efficient, or customised algorithms for computation. The text caters for Masters' and PhD students, as well as postgraduate researchers in artificial intelligence, computer science, statistics, and applied mathematics. Extensive background material is provided along with a wealth of figures, worked examples, and exercises (with solutions) to develop intuition.

定價：3574 元，優惠價：9 3217

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162.

Manifold Learning Theory and Applications

作者：Edited by Yunqian Ma and Yun Fu 出版社：CRC Press UK 出版日：2011/12/01 裝訂：精裝

Trained to extract actionable information from large volumes of high-dimensional data, engineers and scientists often have trouble isolating meaningful low-dimensional structures hidden in their high-

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Learning Scientific Programming with Python

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163.

Learning Scientific Programming with Python

作者：Christian Hill 出版社：Cambridge Univ Pr 出版日：2020/10/31 裝訂：平裝

Learn to master basic programming tasks from scratch with real-life, scientifically relevant examples and solutions drawn from both science and engineering. Students and researchers at all levels are increasingly turning to the powerful Python programming language as an alternative to commercial packages and this fast-paced introduction moves from the basics to advanced concepts in one complete volume, enabling readers to gain proficiency quickly. Beginning with general programming concepts such as loops and functions within the core Python 3 language, and moving on to the NumPy, SciPy and Matplotlib libraries for numerical programming and data visualization, this textbook also discusses the use of Jupyter Notebooks to build rich-media, shareable documents for scientific analysis. The second edition features a new chapter on data analysis with the pandas library and comprehensive updates, and new exercises and examples. A final chapter introduces more advanced topics such as floating-p

定價：2279 元，優惠價：9 2051

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Machine Learning, Optimization, and Big Data ― First International Workshop, Mod 2015, Taormina, Sicily, Italy, July 21-23, 2015, Revised Selected Papers

90折

164.

Machine Learning, Optimization, and Big Data ― First International Workshop, Mod 2015, Taormina, Sicily, Italy, July 21-23, 2015, Revised Selected Papers

作者：Panos Pardalos (EDT); Mario Pavone (EDT); Giovanni Maria Farinella (EDT); Vincenzo Cutello (EDT) 出版社：Springer-Verlag New York Inc 出版日：2016/01/06 裝訂：平裝

This book constitutes revised selected papers from the First International Workshop on Machine Learning, Optimization, and Big Data, MOD 2015, held in Taormina, Sicily, Italy, in July 2015.The 32 pape

定價：3780 元，優惠價：9 3402

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165.

Intelligent Data Analysis for Real-Life Applications—Theory and Practice

作者：Rafael Magdalena-benedito (EDT); Marcelino Martinez-Sober (EDT); Jose Maria Martinez-Martinez (EDT); Pablo Escandell-moreno (EDT) 出版社：Igi Global 出版日：2012/06/30 裝訂：精裝

The 18 papers in this collection explore machine learning techniques for extracting information from large amounts of data with several variables. Six papers from Spanish universities review Bayesian

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Qualitative Data Analysis—A User-Friendly Guide for Social Scientists

90折

166.

Qualitative Data Analysis—A User-Friendly Guide for Social Scientists

作者：Ian Dey 出版社：Taylor & Francis 出版日：1993/05/01 裝訂：平裝

Qualitative Data Analysis shows that learning how to analyse qualitative data by computer can be fun. Written in a stimulating style, with examples drawn mainly from every day life and contemporary hu

定價：3572 元，優惠價：9 3215

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167.

Big Data Analysis ― New Algorithms for a New Society

作者：Nathalie Japkowicz (EDT); Jerzy Stefanowski (EDT) 出版社：Springer-Verlag New York Inc 出版日：2015/12/28 裝訂：精裝

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

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168.

An Introduction to Statistical Methods And Data Analysis

作者：Lyman R. Ott; Micheal Longnecker 出版社：Cengage Learning 出版日：2008/12/26 裝訂：精裝

Ott and Longnecker's AN INTRODUCTION TO STATISTICAL METHODS AND DATA ANALYSIS, Sixth Edition, provides a broad overview of statistical methods for advanced undergraduate and graduate students from a v

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169.

Advanced State Space Methods for Neural and Clinical Data

作者：Zhe Chen 出版社：Cambridge Univ Pr 出版日：2015/10/31 裝訂：精裝

This authoritative work provides an in-depth treatment of state space methods, with a range of applications in neural and clinical data. Advanced and state-of-the-art research topics are detailed, including topics in state space analyses, maximum likelihood methods, variational Bayes, sequential Monte Carlo, Markov chain Monte Carlo, nonparametric Bayesian, and deep learning methods. Details are provided on practical applications in neural and clinical data, whether this is characterising time series data from neural spike trains recorded from the rat hippocampus, the primate motor cortex, or the human EEG, MEG or fMRI, or physiological measurements of heartbeats or blood pressures. With real-world case studies of neuroscience experiments and clinical data sets, and written by expert authors from across the field, this is an ideal resource for anyone working in neuroscience and physiological data analysis.

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170.

Data Analysis and Graphics Using R ─ An Example-Based Approach

作者：John Maindonald 出版社：Cambridge Univ Pr 出版日：2010/06/07 裝訂：精裝

Discover what you can do with R! Introducing the R system, covering standard regression methods, then tackling more advanced topics, this book guides users through the practical, powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display, and interpretation of data. The many worked examples, from real-world research, are accompanied by commentary on what is done and why. The companion website has code and datasets, allowing readers to reproduce all analyses, along with solutions to selected exercises and updates. Assuming basic statistical knowledge and some experience with data analysis (but not R), the book is ideal for research scientists, final-year undergraduate or graduate-level students of applied statistics, and practising statisticians. It is both for learning and for reference. This third edition expands upon topics such as Bayesian inference for regression, errors in variables, generalized linear mixed models, and random forests.

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171.

The Text Mining Handbook：Advanced Approaches in Analyzing Unstructured Data

作者：Ronen Feldman 出版社：Cambridge Univ Pr 出版日：2006/12/11 裝訂：精裝

Text mining is a new and exciting area of computer science research that tries to solve the crisis of information overload by combining techniques from data mining, machine learning, natural language processing, information retrieval, and knowledge management. Similarly, link detection – a rapidly evolving approach to the analysis of text that shares and builds upon many of the key elements of text mining – also provides new tools for people to better leverage their burgeoning textual data resources. The Text Mining Handbook presents a comprehensive discussion of the state-of-the-art in text mining and link detection. In addition to providing an in-depth examination of core text mining and link detection algorithms and operations, the book examines advanced pre-processing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection in such varied fields a

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172.

Signal Processing and Networking for Big Data Applications

作者：Zhu Han 出版社：Cambridge Univ Pr 出版日：2017/06/30 裝訂：精裝

This unique text helps make sense of big data in engineering applications using tools and techniques from signal processing. It presents fundamental signal processing theories and software implementations, reviews current research trends and challenges, and describes the techniques used for analysis, design and optimization. Readers will learn about key theoretical issues such as data modelling and representation, scalable and low-complexity information processing and optimization, tensor and sublinear algorithms, and deep learning and software architecture, and their application to a wide range of engineering scenarios. Applications discussed in detail include wireless networking, smart grid systems, and sensor networks and cloud computing. This is the ideal text for researchers and practising engineers wanting to solve practical problems involving large amounts of data, and for students looking to grasp the fundamentals of big data analytics.

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Predictive Analytics for Marketers ─ Using Data Mining for Business Advantage

90折

173.

Predictive Analytics for Marketers ─ Using Data Mining for Business Advantage

作者：Barry Leventhal 出版社：Kogan Page Ltd 出版日：2018/02/28 裝訂：平裝

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

定價：1575 元，優惠價：9 1418

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174.

Computational Texture and Patterns: From Textons to Deep Learning (Synthesis Lectures on Computer Vision)

作者：Kristin J. Dana; Gerard Medioni ; Sven Dickinson 出版社：Morgan & Claypool 出版日：2018/09/13 裝訂：精裝

Visual pattern analysis is a fundamental tool in mining data for knowledge. Computational representations for patterns and texture allow us to summarize, store, compare, and label in order to learn ab

定價：2248 元，優惠價：1 2248

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175.

Crosslinguistic Influence and Distinctive Patterns of Language Learning ─ Findings and Insights from a Learner Corpus

作者：Anne Golden (EDT); Scott Jarvis (EDT); Kari Tenfjord (EDT) 出版社：Multilingual Matters Ltd 出版日：2017/09/30 裝訂：精裝

This book details patterns of language use found in the writing of adult learners of Norwegian as a second language (L2). Each study draws its data from the same corpus of L2 Norwegian texts and exami

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Distributed Machine Learning with Pyspark: Migrating Effortlessly from Pandas and Scikit-Learn

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176.

Distributed Machine Learning with Pyspark: Migrating Effortlessly from Pandas and Scikit-Learn

作者：Abdelaziz Testas 出版社：Apress 出版日：2023/11/12 裝訂：平裝

Migrate from pandas and scikit-Learn to PySpark to handle vast amounts of data and achieve faster data processing time. This book will show you how to make this transition by adapting your skills and leveraging the similarities in syntax, functionality, and interoperability between these tools. Distributed Machine Learning with PySpark offers a roadmap to data scientists considering transitioning from small data libraries (pandas/scikit-learn) to big data processing and machine learning with PySpark. You will learn to translate Python code from pandas/scikit-learn to PySpark to preprocess large volumes of data and build, train, test, and evaluate popular machine learning algorithms such as linear and logistic regression, decision trees, random forests, support vector machines, Na鴳e Bayes, and neural networks. After completing this book, you will understand the foundational concepts of data preparation and machine learning and will have the skills necessary to apply these methods using

定價：1925 元，優惠價：1 1925

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Machine Learning and Its Applications—Advanced Lectures

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177.

Machine Learning and Its Applications—Advanced Lectures

作者：Georgios Paliouras (EDT); Vangelis Karkaletsis (EDT); Constantine D. Spyropoulos (EDT) 出版社：Springer-Verlag New York Inc 出版日：2001/09/01 裝訂：平裝

In recent years machine learning has made its way from artificial intelligence into areas of administration, commerce, and industry. Data mining is perhaps the most widely known demonstration of this

定價：3148 元，優惠價：1 3148

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Machine Learning, revised and updated edition

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178.

Machine Learning, revised and updated edition

作者：Ethem Alpaydin 出版社：Mit Pr 出版日：2021/08/17 裝訂：平裝

A concise overview of machine learning--computer programs that learn from data--the basis of such applications as voice recognition and driverless cars.Today, machine learning underlies a range of app

定價：558 元，優惠價：79 441

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Critically Engaged Learning: Connecting to Young Lives

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179.

Critically Engaged Learning: Connecting to Young Lives

作者：John Smyth; Lawrence Angus; Barry Down; Peter McInerney 出版社：Peter Lang Pub Inc 出版日：2008/08/01 裝訂：平裝

Basing their ideas upon empirical data gathered from 2005 to 2008 in Australia, the authors (Smyth, Angus and McInerney, education, U. of Ballarat; Down, education, Murdoch U.) move beyond the usual p

定價：2592 元，優惠價：1 2592

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180.

Computer Vision ─ Models, Learning, and Inference

作者：Simon J. D. Prince 出版社：Cambridge Univ Pr 出版日：2012/06/18 裝訂：精裝

This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to learn the relationships between the observed image data and the aspects of the world that we wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences about the world from new image data. With minimal prerequisites, the book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build useful vision systems. Primarily meant for advanced undergraduate and graduate students, the detailed methodological presentation will also be useful for practitioners of computer vision. • Covers cutting-edge techniques, including graph cuts, machine learning and multiple view geometry • A unified approach shows the common basis for solutions of important computer vision problems, such as camera calibration, f

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Probabilistic Numerics：Computation as Machine Learning

Manifold Learning Theory and Applications

Learning Scientific Programming with Python

Machine Learning, Optimization, and Big Data ― First International Workshop, Mod 2015, Taormina, Sicily, Italy, July 21-23, 2015, Revised Selected Papers

Intelligent Data Analysis for Real-Life Applications—Theory and Practice

Qualitative Data Analysis—A User-Friendly Guide for Social Scientists

Big Data Analysis ― New Algorithms for a New Society

An Introduction to Statistical Methods And Data Analysis

Advanced State Space Methods for Neural and Clinical Data

Data Analysis and Graphics Using R ─ An Example-Based Approach

The Text Mining Handbook：Advanced Approaches in Analyzing Unstructured Data

Signal Processing and Networking for Big Data Applications

Predictive Analytics for Marketers ─ Using Data Mining for Business Advantage

Computational Texture and Patterns: From Textons to Deep Learning (Synthesis Lectures on Computer Vision)

Crosslinguistic Influence and Distinctive Patterns of Language Learning ─ Findings and Insights from a Learner Corpus

Distributed Machine Learning with Pyspark: Migrating Effortlessly from Pandas and Scikit-Learn

Machine Learning and Its Applications—Advanced Lectures

Machine Learning, revised and updated edition

Critically Engaged Learning: Connecting to Young Lives

Computer Vision ─ Models, Learning, and Inference