Graphical Models for Machine Learning and Digital Communication

A variety of problems in machine learning and digital communication deal with complexbut structured natural or artificial systems. In this book, Brendan Frey uses graphical models as anoverarching fra

"This book identifies the role of digital technology in personalized learning, brings together research in personalized learning with a focus on cognitive, affective and metacogntive functionality of

Asian Celebrity Cultures in the Digital Age represents the first comprehensive study on the transformations of celebrity cultures in increasingly globalised and digitalised Asian societies. It discusses relations between Asian celebrities and digital media across emerging phenomena in celebrity practices, cultures, politics, fandom, and economies. Highlighting original case studies from prominent Asian societies, including India, China, Hong Kong, the Philippines, Singapore, Vietnam, Indonesia, Thailand, South Korea, and Japan, this book sheds much-needed light on the de-Westernisation and internationalisation of celebrity studies and is essential reading for scholars and students in celebrity, fandom, digital media and communication, and cultural studies.

A bright and engaging first information book about farms, specially designed for children learning to read.FARM unlocks a free 16-page digital companion book, FARM FUN, offering even more information,

With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are systematically organised by their machine learning nature, including classification, sequence labelling, and sequence-to-sequence problems. Topics covered include statistical machine learning and deep learning models, text classification and structured prediction models, generative and discriminative models, supervised and unsupervised learning with latent variables, neural networks, and transition-based methods. Rich connections are drawn between concepts throughout the book, equipping students with the tools needed to establish a deep understanding of NLP solutions, adapt existing models, and confidently develop innovative models of their own. Featuring a host of examples, intuition, and end of chapter exercises, plus sample code available as an onli

This book will help readers understand fundamental and advanced statistical models and deep learning models for robust speaker recognition and domain adaptation. This useful toolkit enables readers to apply machine learning techniques to address practical issues, such as robustness under adverse acoustic environments and domain mismatch, when deploying speaker recognition systems. Presenting state-of-the-art machine learning techniques for speaker recognition and featuring a range of probabilistic models, learning algorithms, case studies, and new trends and directions for speaker recognition based on modern machine learning and deep learning, this is the perfect resource for graduates, researchers, practitioners and engineers in electrical engineering, computer science and applied mathematics.

This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random for

Master alternative Big Data technologies that can do what Hadoop can't: real-time analytics and iterative machine learning. When most technical professionals think of Big Data analytics today, th

This book provides a general framework for learning sparse graphical models with conditional independence tests. It includes complete treatments for Gaussian, Poisson, multinomial, and mixed data; uni

Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning methods for neuroscience. The methods are demonstrated through case studies of real problems to empower readers to build their own solutions. The book covers a wide variety of methods, including supervised classification with non-probabilistic models (nearest-neighbors, classification trees, rule induction, artificial neural networks and support vector machines) and probabilistic models (discriminant analysis, logistic regression and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers and feature subset selection methods. Other parts of the book are devoted to association discovery with probabilistic graphical models (Bayesian networks and Markov networks) and spatial statistics with po

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every cha

This book bridges theoretical computer science and machine learning by exploring what the two sides can teach each other. It emphasizes the need for flexible, tractable models that better capture not what makes machine learning hard, but what makes it easy. Theoretical computer scientists will be introduced to important models in machine learning and to the main questions within the field. Machine learning researchers will be introduced to cutting-edge research in an accessible format, and gain familiarity with a modern, algorithmic toolkit, including the method of moments, tensor decompositions and convex programming relaxations. The treatment beyond worst-case analysis is to build a rigorous understanding about the approaches used in practice and to facilitate the discovery of exciting, new ways to solve important long-standing problems.

Graphical models are of increasing importance in applied statistics, and in particular in data mining. Providing a self-contained introduction and overview to learning relational, probabilistic, and p

Machine Learning: Discriminative and Generative covers the main contemporary themes and tools in machine learning ranging from Bayesian probabilistic models to discriminative support-vector machines.

Learning to spell really can be fun, especially when you have an amazing Spelling Machine, a 2-book series for preschoolers ages 4-6 to learn spelling and start to read. Within each scene there are five objects to find and learn to spell. Flip out your magical spelling machine contained inside the book, dial in the letters of each word and lift the flap to check your answer!※無音檔英文學習遊戲書有了spelling machine的神奇轉盤，拼單字，也可以變得超級有趣！全新包裝，內容再升級知名親子叢書作家廖彩杏推薦：“ Spelling Machine：Make Spelling Fun！”是一本寓教於樂的遊戲書，讓孩子從小小的拼字遊戲，得到樂趣。本套書以「度假」為主題，分6大場景，共收錄30個常用英文單字。在不同場景，小朋友要先動手動眼，找一找不同物件在哪裡？並學會認識這些物件的英文，學會認字後，用前方的轉盤練習拼字。剛開始認字、學習自然發音的小讀者，都能從遊戲中開心學習！ 【玩法說明】1.第一步，先把紅圈順時針轉到「start」處。2.假設今天要讓孩子熟悉crab的拼法，就先大聲念出crab，讓孩子聽聽看，這個單字，是由哪些字母所組成的。3.現在要拼出crab，就把第一個字母c，順時針轉到start處。4.再把第二個字母r，撥到start處，接下來的字母以此類推。5.最後.恭喜，完成了！請孩子打開翻頁機關確認答案，看到「crab」了嗎？ 是不是好像變魔術一樣，好神奇呢？拼字，也可以這麼有趣！

Perfect for children ages 3-6 who are learning about their feelings, Learning Kindness My Feelings Sticker Book features over 50 reusable, vinyl-cling stickers, a dry-erase marker, and tons of fun activities to help kids explore their emotions.This engaging activity book is packed with puzzles and exercises that teach kids how to understand emotions in themselves and others. Kids can use the stickers with the guided activities to identify and express their feelings, recognize facial expressions and body language, and learn how to regulate their emotions. This book is an excellent tool for parents and educators to deepen conversations with children around social and emotional thinking.

Imagine learning to leap, roar, swim, or fly for the first time.Born to Be Wild explores the vast complexities of growing up in the animal kingdom, where everything feels like new. This book goes where no other has to delve into the countless ways baby animals survive in the wild. Alongside gorgeous photographs of baby bears, lions and tigers, marsupials, birds, and marine mammals, it charts the stages from dependency to independence, and looks at why some animals cling to their mothers for many months, while others must learn to run almost as soon as they are born.Informative step-by-step features focus on particular stages of learning, such as how a lion cub learns to hunt. This book focuses on key themes in a young animal’s life: birth and the very first days of life, learning to hunt and feed, senses and communication, habitat and adaptation, and playtime. Full of beautiful pictures and fascinating science, Born to Be Wild reveals the mysteries of how young creatures survive in an

Your home is the perfect place for learning, fun, and sibling bonding!The Happy Learning Book for Siblings features 50 hands-on activities you can conduct in the comfort of your home. They are divided

Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin the design of personalized predictive models for a variety of settings and modalities. The book begins by revising 'traditional' machine learning models, focusing on adapting them to settings involving user data, then presents techniques based on advanced principles such as matrix factorization, deep learning, and generative modeling, and concludes with a detailed study of the consequences and risks of deploying personalized predictive systems. A series of case studies in domains ranging from e-commerce to health plus hands-on projects and code examples will give readers understanding and experience with large-scale real-world datasets and the ability to design mod

Recent years have witnessed an explosion in the volume and variety of data collected in all scientific disciplines and industrial settings. Such massive data sets present a number of challenges to researchers in statistics and machine learning. This book provides a self-contained introduction to the area of high-dimensional statistics, aimed at the first-year graduate level. It includes chapters that are focused on core methodology and theory - including tail bounds, concentration inequalities, uniform laws and empirical process, and random matrices - as well as chapters devoted to in-depth exploration of particular model classes - including sparse linear models, matrix models with rank constraints, graphical models, and various types of non-parametric models. With hundreds of worked examples and exercises, this text is intended both for courses and for self-study by graduate students and researchers in statistics, machine learning, and related fields who must understand, apply, and ad

Graphical models are a statistical tool used for a wide range of applications. There has been a huge amount of research in this topic across statistics, mathematics and computer science in the last fe

Statistical Regression and Classification: From Linear Models to Machine Learning takes an innovative look at the traditional statistical regression course, presenting a contemporary treatment in line

"This book provides an introduction to computational trust models from a machine learning perspective. After reviewing traditional computational trust models, it discusses a new trend of applying form

Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the kernelization of learning models. In addition, several other theorems are devoted to proving mathematical equivalence between seemingly unrelated models. With over 25 closed-form and iterative algorithms, the book provides a step-by-step guide to algorithmic procedures and analysing which factors to consider in tackling a given problem, enabling readers to improve specifically designed learning algorithms, build models for new applications and develop efficient techniques suitable for green machine learning technologies. Numerous real-world examples and over 200 problems, several of which are Matlab-based simulation exercises, make this an essential resource for graduate student

The Cosmic Adventures of Astrid and Stella Star Struck! (the Cosmic Adventures of Astrid and Stella Book #2)Get Outer My Space! (the Cosmic Adventures of Astrid and Stella Book #3)BESTSELLING TEAM: Sabrina and Eunice Moyle are the team behind Hello!Lucky. Their books have sold nearly 1.5 million copies across formats, and they boast nearly 78,000 followers on Instagram.HIGH-DEMAND FORMAT: Graphic novels for emerging readers are an in-demand category that’s only growing. With three easily digestible stories per book, this new series is sure to satisfy.EVERGREEN THEMES: Friendship, consent, teamwork, communication, and more make an appearance in the pages of this latest outing from the social-emotional learning pros behind Hello!Lucky.Blast off with Astrid & Stella in Get Outer My Space, the third book in the full-color young readers graphic novel series from the bestselling creators of Hello!Lucky, Sabrina and Eunice Moyle, and perfect for fans of Narwhal and Jelly.

Preschoolers are introduced to core science concepts with this playful format featuring 20 hands-on activities that use everyday household materials, including a vinegar volcano, a hula hoop count, and a kitchen sink or swim experiment. In this fourth book in the Busy Little Hands series, preschoolers get ready for a science adventure! Preschoolers wonder and explore with 20 hands-on experiments using everyday household objects and making daily activities such as snack time and play time into learning opportunities. Each play activity demonstrates a simple principle of physics, earth science, chemistry, or biology, including the Kitchen Sink or Float (demonstrating density), the Vinegar Volcano (pressure) and Blooming Colors (chromatography). Featuring bright, easy-to-follow photos specially designed for pre-readers, this book is packed with learning fun, plus it sets the groundwork for science success in kindergarten and beyond.

風靡全球的兒少／成人科普繪本物理學家也讚嘆，量子物理原來可以這樣理解！★獲獎紀錄豐富－2018年英國少年雜誌最佳兒童設計／圖書銀獎 (Silver for Best Designed/Illustrated Book for Children)－2018年英國圖書設計與製作獎 (British Book Design and Production Awards)－入圍2019美國科學促進會／速霸陸汽車公司頒發的科學圖書優等獎（中級科學圖書類）(the 2019 AAAS/Subaru SB&F Prize for Excellence in Science Books (Middle Grades Science Book category))－獲選為「速霸陸愛學習計畫」(Subaru Loves Learning Program) 教材圖書，被開發為免費教材在全美進行推廣－獲選第79梯次「好書大家讀」 優良少年兒童讀物★各國爭相出版西班牙文、英文、法文、義大利文、德文、簡體中文、韓文、俄文等多種語言版本陸續出版，深受各國讀者喜愛。★亞馬遜網路書店評價 4.7 顆星，盛讚如潮！「圖畫可愛有趣，文字清晰易讀，作為一名科學家，我發現了以前我不知道的事情。」──亞馬遜讀者 5 星評論「如果你想讓孩子變聰明，這本書是必須的！」──亞馬遜 5 星評論★當當網好評率100%，點讚數直逼2萬！「不要低估小學生的理解力，我家那位竟然看得津津有味，還說要去查查普朗克是如何攻克什麼的，總之把老母親哄得一愣一愣，這本書他非常喜歡。」──當當網 10 星評論「這本書每一頁都有愛因斯坦老師給孩子講一些物理知識，兒子卡住不懂的時候會問我，其實我有些知識也是跟他一起讀這本書理解的。」──當當網 10 星評論【本書簡介】我們周圍的一切，包含我們自己，都是由小於奈米的微小顆粒組成。量子物理學是這些粒子的科學，沒有它，我們的電子設備都不存在。量子粒子的特性和行為很奇妙，和我們日常生活中所見的物體完全不同。透過這本豐富的圖解指南，愛因斯坦博士將帶領我們從牛頓的古典物理開始，介紹到普朗克的量子、光的波粒二象性、物質和反物質、量子糾纏、薛丁格的貓……帶你進入神秘又有趣的量子世界。【本書特色】◎獨一無二！量子物理啟蒙繪本量子物理在大多數的成人眼裡好比外星語言，本書卻從兒童的視角出發，把艱澀的原理變得簡

From Miles Young, worldwide non-executive chairman of Ogilvy & Mather, comes a sequel to David Ogilvy's bestselling advertising handbook featuring essential strategies for the digital age.In this

Nigel Barker—fashion authority, photographer, and host of Oxygen’s The Face—presents 50 of the most influential models from the 1940s to today through a wealth of full-color photographs from the world

The Classic Guide to Business Communications . . . Updated for a New Generation of Media-Savvy LeadersEven with the latest high-tech tools and communication options, the simple truth is this: You need

SPELLING MACHINE features a unique wheel mechanism that enables kids to dial letters to spell words. A simple flap hides the word until the child is finished dialing. Lift up the flap to see if the wo

"An indispensable primer for those who want to protect their digital rights from the dark forces of big media." -Kara Swisher, author of aol.com The first general interest book by a blogger edited col

趁著哈洛小森林的動物們正在慶祝著四季的交替和各種重要的節日時，讓我們隨著他們的腳步，一同走進這些活潑的小故事中探險吧！中英對照，讓你 365天都有新鮮有趣的故事。 There^s an adventure for every day of the year, and you can read the stories one at a time or in bunches. If you like

趁著哈洛小森林的動物們正在慶祝著四季的交替和各種重要的節日時，讓我們隨著他們的腳步，一同走進這些活潑的小故事中探險吧！中英對照，讓你365天都有新鮮有趣的故事。 There^s an adventure for every day of the year, and you can read the stories one at a time or in bunches. If you lik

趁著哈洛小森林的動物們正在慶祝著四季的交替和各種重要的節日時，讓我們隨著他們的腳步，一同走進這些活潑的小故事中探險吧！中英對照，讓你365天都有新鮮有趣的故事。 There^s an adventure for every day of the year, and you can read the stories one at a time or in bunches. If you lik

The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts. New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning.

This Book discusses machine learning for model order reduction, which can be used in modern VLSI design to predict the behavior of an electronic circuit, via mathematical models that predict behavior.

Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors,

'What's going to happen next?' Time series data hold the answers, and Bayesian methods represent the cutting edge in learning what they have to say. This ambitious book is the first unified treatment of the emerging knowledge-base in Bayesian time series techniques. Exploiting the unifying framework of probabilistic graphical models, the book covers approximation schemes, both Monte Carlo and deterministic, and introduces switching, multi-object, non-parametric and agent-based models in a variety of application environments. It demonstrates that the basic framework supports the rapid creation of models tailored to specific applications and gives insight into the computational complexity of their implementation. The authors span traditional disciplines such as statistics and engineering and the more recently established areas of machine learning and pattern recognition. Readers with a basic understanding of applied probability, but no experience with time series analysis, are guided fro

Machine learning methods originated from artificial intelligence and are now used in various fields in environmental sciences today. This is the first single-authored textbook providing a unified treatment of machine learning methods and their applications in the environmental sciences. Due to their powerful nonlinear modelling capability, machine learning methods today are used in satellite data processing, general circulation models(GCM), weather and climate prediction, air quality forecasting, analysis and modelling of environmental data, oceanographic and hydrological forecasting, ecological modelling, and monitoring of snow, ice and forests. The book includes end-of-chapter review questions and an appendix listing websites for downloading computer code and data sources. A resources website contains datasets for exercises, and password-protected solutions are available. The book is suitable for first-year graduate students and advanced undergraduates. It is also valuable for resear