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Author: Philip D. Laird
Publisher: Springer Science & Business Media
ISBN: 1461316855
Category : Computers
Languages : en
Pages : 223
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Book Description
This monograph is a contribution to the study of the identification problem: the problem of identifying an item from a known class us ing positive and negative examples. This problem is considered to be an important component of the process of inductive learning, and as such has been studied extensively. In the overview we shall explain the objectives of this work and its place in the overall fabric of learning research. Context. Learning occurs in many forms; the only form we are treat ing here is inductive learning, roughly characterized as the process of forming general concepts from specific examples. Computer Science has found three basic approaches to this problem: • Select a specific learning task, possibly part of a larger task, and construct a computer program to solve that task . • Study cognitive models of learning in humans and extrapolate from them general principles to explain learning behavior. Then construct machine programs to test and illustrate these models. xi Xll PREFACE • Formulate a mathematical theory to capture key features of the induction process. This work belongs to the third category. The various studies of learning utilize training examples (data) in different ways. The three principal ones are: • Similarity-based (or empirical) learning, in which a collection of examples is used to select an explanation from a class of possible rules.
Author: Philip D. Laird
Publisher: Springer Science & Business Media
ISBN: 1461316855
Category : Computers
Languages : en
Pages : 223
Get Book Here
Book Description
This monograph is a contribution to the study of the identification problem: the problem of identifying an item from a known class us ing positive and negative examples. This problem is considered to be an important component of the process of inductive learning, and as such has been studied extensively. In the overview we shall explain the objectives of this work and its place in the overall fabric of learning research. Context. Learning occurs in many forms; the only form we are treat ing here is inductive learning, roughly characterized as the process of forming general concepts from specific examples. Computer Science has found three basic approaches to this problem: • Select a specific learning task, possibly part of a larger task, and construct a computer program to solve that task . • Study cognitive models of learning in humans and extrapolate from them general principles to explain learning behavior. Then construct machine programs to test and illustrate these models. xi Xll PREFACE • Formulate a mathematical theory to capture key features of the induction process. This work belongs to the third category. The various studies of learning utilize training examples (data) in different ways. The three principal ones are: • Similarity-based (or empirical) learning, in which a collection of examples is used to select an explanation from a class of possible rules.
Author: Q. Ethan McCallum
Publisher: "O'Reilly Media, Inc."
ISBN: 1449324975
Category : Computers
Languages : en
Pages : 265
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Book Description
What is bad data? Some people consider it a technical phenomenon, like missing values or malformed records, but bad data includes a lot more. In this handbook, data expert Q. Ethan McCallum has gathered 19 colleagues from every corner of the data arena to reveal how they’ve recovered from nasty data problems. From cranky storage to poor representation to misguided policy, there are many paths to bad data. Bottom line? Bad data is data that gets in the way. This book explains effective ways to get around it. Among the many topics covered, you’ll discover how to: Test drive your data to see if it’s ready for analysis Work spreadsheet data into a usable form Handle encoding problems that lurk in text data Develop a successful web-scraping effort Use NLP tools to reveal the real sentiment of online reviews Address cloud computing issues that can impact your analysis effort Avoid policies that create data analysis roadblocks Take a systematic approach to data quality analysis
Author: Cole Nussbaumer Knaflic
Publisher: John Wiley & Sons
ISBN: 1119002265
Category : Mathematics
Languages : en
Pages : 284
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Book Description
Don't simply show your data—tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation. Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to: Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—Storytelling with Data will give you the skills and power to tell it!
Author: Joel Grus
Publisher: "O'Reilly Media, Inc."
ISBN: 1491904399
Category : Computers
Languages : en
Pages : 408
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Book Description
Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases
Author: John Kuhn
Publisher: Teachers College Press
ISBN: 0807755729
Category : Education
Languages : en
Pages : 177
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Book Description
In this moving account, Americas Superintendent John Kuhn lays bare the scare tactics at the root of the modern school reform movement. Kuhn conveys a deeply held passion for the mission and promise of public education through his own experience as a school administrator in Texas. When his Alamo Letter first appeared in the Washington Post, it galvanized the educational community in a call to action that was impossible to ignore. This powerful book requires us to question whether the current education crisis will be judged by history as a legitimate national emergency or an agenda-driven panic, spurred on by a media that is, for the most part, uninterested in anything but useless soundbites. Essential reading for teachers, administrators, policymakers, and everyone concerned with public education, Fear and Learning in America: Analyzes school reform from the perspective of a practicing school administrator who isnt sold on the corporate reform package.Places school reform in the historical context of similar episodes of national hand-wringing.Offers encouragement and appreciation to classroom teachers who are exhausted by the vilification that modern school reform has served up. John Kuhns book is packed with more wisdom than any 10 books that I have read about American education. It is the wisdom born of experience. It is the wisdom of a man who cares about children, families, and community. From the Foreword by Diane Ravitch, author of Reign of Error In Fear and Learning in America, John Kuhn weaves together stories from his life as a teacher and missionary with tales from history. The result is a fresh way of thinking about schools and educational policy. Refuting A Nation at Risk, Mr. Kuhn warns, persuasively, of a rising tide of inequality. His message, artfully delivered in this important book, should be heeded. John Merrow, education correspondent, PBS NewsHour, and president, Learning Matters, Inc. Kuhn is a superb educator and his valuable book effectively dissects the myths about todays high-stakes testing environment and the worsening conditions under which educators are expected to make miracles every day, or else. His cogent arguments against such policies demonstrate what is really important and should inform the debate about public education. Randi Weingarten, president, American Federation of Teachers This book is a brilliantly clear defense of public education as our nations most valuable asset. John Kuhn fearlessly names the fact that todays education reformers, like the renowned emperor, wear no clothes. Christine Sleeter, professor emerita, California State University Monterey Bay
Author: Sally Campbell Galman
Publisher: Routledge
ISBN: 9781598746327
Category : Ethnology
Languages : en
Pages : 0
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Book Description
An entertaining introductory guide to conducting qualitative data analysis in comic book format, following the character of Shane the Lone Ethnographer.
Author: Angela Daly
Publisher: Lulu.com
ISBN: 9492302284
Category : Data protection
Languages : en
Pages : 372
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Book Description
Moving away from the strong body of critique of pervasive ?bad data? practices by both governments and private actors in the globalized digital economy, this book aims to paint an alternative, more optimistic but still pragmatic picture of the datafied future. The authors examine and propose ?good data? practices, values and principles from an interdisciplinary, international perspective. From ideas of data sovereignty and justice, to manifestos for change and calls for activism, this collection opens a multifaceted conversation on the kinds of futures we want to see, and presents concrete steps on how we can start realizing good data in practice.
Author: Peter Schryvers
Publisher: Rowman & Littlefield
ISBN: 1633885917
Category : Business & Economics
Languages : en
Pages : 353
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Book Description
Highlights the pitfalls of data analysis and emphasizes the importance of using the appropriate metrics before making key decisions.Big data is often touted as the key to understanding almost every aspect of contemporary life. This critique of "information hubris" shows that even more important than data is finding the right metrics to evaluate it.The author, an expert in environmental design and city planning, examines the many ways in which we measure ourselves and our world. He dissects the metrics we apply to health, worker productivity, our children's education, the quality of our environment, the effectiveness of leaders, the dynamics of the economy, and the overall well-being of the planet. Among the areas where the wrong metrics have led to poor outcomes, he cites the fee-for-service model of health care, corporate cultures that emphasize time spent on the job while overlooking key productivity measures, overreliance on standardized testing in education to the detriment of authentic learning, and a blinkered focus on carbon emissions, which underestimates the impact of industrial damage to our natural world. He also examines various communities and systems that have achieved better outcomes by adjusting the ways in which they measure data. The best results are attained by those that have learned not only what to measure and how to measure it, but what it all means. By highlighting the pitfalls inherent in data analysis, this illuminating book reminds us that not everything that can be counted really counts.
Author: Hadley Wickham
Publisher: "O'Reilly Media, Inc."
ISBN: 1491910364
Category : Computers
Languages : en
Pages : 521
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Book Description
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
Author: Doug Fisher
Publisher: Springer Science & Business Media
ISBN: 1461224047
Category : Mathematics
Languages : en
Pages : 444
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Book Description
Ten years ago Bill Gale of AT&T Bell Laboratories was primary organizer of the first Workshop on Artificial Intelligence and Statistics. In the early days of the Workshop series it seemed clear that researchers in AI and statistics had common interests, though with different emphases, goals, and vocabularies. In learning and model selection, for example, a historical goal of AI to build autonomous agents probably contributed to a focus on parameter-free learning systems, which relied little on an external analyst's assumptions about the data. This seemed at odds with statistical strategy, which stemmed from a view that model selection methods were tools to augment, not replace, the abilities of a human analyst. Thus, statisticians have traditionally spent considerably more time exploiting prior information of the environment to model data and exploratory data analysis methods tailored to their assumptions. In statistics, special emphasis is placed on model checking, making extensive use of residual analysis, because all models are 'wrong', but some are better than others. It is increasingly recognized that AI researchers and/or AI programs can exploit the same kind of statistical strategies to good effect. Often AI researchers and statisticians emphasized different aspects of what in retrospect we might now regard as the same overriding tasks.
