The If Machine, 2nd edition

2019-07-25
The If Machine, 2nd edition

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  • Author: Peter Worley
  • Publisher: Bloomsbury Publishing
  • ISBN: 1472969065
  • Category : Education
  • Languages : en
  • Pages : 232

Fully updated edition with five brand new sessions and bonus features. Fully updated with new developments in the theory and practice of teaching philosophy, this new edition of the bestselling book, The If Machine, presents 30 clear, ready-to-use plans to teach philosophy in the classroom with children aged five to 13. Each tried-and-tested session offers an imaginary situation, followed by a series of questions to encourage children to challenge key philosophical ideas such as values and ethics, gender and identity, and existence and beauty. With a star system indicating the level of difficulty, this practical book by Peter Worley, founder of The Philosophy Foundation, outlines Peter's philosophical enquiry method, which he has developed over 20 years of teaching. This fantastic resource also includes a new hints and tips section, as well as a troubleshooting table with guidance and links to further resources about how to deal with common problems such as unanimity. Accompanied by a companion website featuring an introduction to the philosophy that inspired the ideas, The If Machine is a must-have resource for all classrooms.

The If Machine

2010-12-23
The If Machine

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  • Author: Peter Worley
  • Publisher: Bloomsbury Publishing
  • ISBN: 1441136827
  • Category : Education
  • Languages : en
  • Pages : 216

Each session in this practical book offers an imaginary situation, followed by a series of questions to encourage children to challenge key philosophical ideas such as values and ethics, gender and identity, and existence and beauty. All the enquiries have been tried and tested, and a handy star system is included to indicate the difficulty level of each one. With a comprehensive introduction and key sections on the philosophy behind the experiments, this book also includes an online teacher's resource to guide practitioners through using the sessions to best effect in the classroom.

Foundations of Machine Learning, second edition

2018-12-25
Foundations of Machine Learning, second edition

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  • Author: Mehryar Mohri
  • Publisher: MIT Press
  • ISBN: 0262351366
  • Category : Computers
  • Languages : en
  • Pages : 505

A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.

Machine Learning

2011-03-23
Machine Learning

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  • Author: Stephen Marsland
  • Publisher: CRC Press
  • ISBN: 1420067192
  • Category : Business & Economics
  • Languages : en
  • Pages : 407

Traditional books on machine learning can be divided into two groups- those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but

The If Odyssey

2012-12-06
The If Odyssey

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  • Author: Peter Worley
  • Publisher: A&C Black
  • ISBN: 1441174958
  • Category : Education
  • Languages : en
  • Pages : 204

Using Homer's epic tales as inspiration, this book offers teachers session plans and storytelling tips to facilitate philosophical discussions with children aged 9-14.

Introduction to Machine Learning

2014-08-22
Introduction to Machine Learning

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  • Author: Ethem Alpaydin
  • Publisher: MIT Press
  • ISBN: 0262028182
  • Category : Computers
  • Languages : en
  • Pages : 639

Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Kernel machines -- Graphical models -- Brief contents -- Hidden markov models -- Bayesian estimation -- Combining multiple learners -- Reinforcement learning -- Design and analysis of machine learning experiments.

Data Mining

2011-02-03
Data Mining

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  • Author: Ian H. Witten
  • Publisher: Elsevier
  • ISBN: 0080890369
  • Category : Computers
  • Languages : en
  • Pages : 665

Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise. Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Artificial Intelligence By Example

2020-02-28
Artificial Intelligence By Example

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  • Author: Denis Rothman
  • Publisher: Packt Publishing Ltd
  • ISBN: 1839212810
  • Category : Computers
  • Languages : en
  • Pages : 579

Understand the fundamentals and develop your own AI solutions in this updated edition packed with many new examples Key FeaturesAI-based examples to guide you in designing and implementing machine intelligenceBuild machine intelligence from scratch using artificial intelligence examplesDevelop machine intelligence from scratch using real artificial intelligenceBook Description AI has the potential to replicate humans in every field. Artificial Intelligence By Example, Second Edition serves as a starting point for you to understand how AI is built, with the help of intriguing and exciting examples. This book will make you an adaptive thinker and help you apply concepts to real-world scenarios. Using some of the most interesting AI examples, right from computer programs such as a simple chess engine to cognitive chatbots, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and Internet of Things (IoT), and develop emotional quotient in chatbots using neural networks such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs). This edition also has new examples for hybrid neural networks, combining reinforcement learning (RL) and deep learning (DL), chained algorithms, combining unsupervised learning with decision trees, random forests, combining DL and genetic algorithms, conversational user interfaces (CUI) for chatbots, neuromorphic computing, and quantum computing. By the end of this book, you will understand the fundamentals of AI and have worked through a number of examples that will help you develop your AI solutions. What you will learnApply k-nearest neighbors (KNN) to language translations and explore the opportunities in Google TranslateUnderstand chained algorithms combining unsupervised learning with decision treesSolve the XOR problem with feedforward neural networks (FNN) and build its architecture to represent a data flow graphLearn about meta learning models with hybrid neural networksCreate a chatbot and optimize its emotional intelligence deficiencies with tools such as Small Talk and data loggingBuilding conversational user interfaces (CUI) for chatbotsWriting genetic algorithms that optimize deep learning neural networksBuild quantum computing circuitsWho this book is for Developers and those interested in AI, who want to understand the fundamentals of Artificial Intelligence and implement them practically. Prior experience with Python programming and statistical knowledge is essential to make the most out of this book.

Python Machine Learning

2015-09-23
Python Machine Learning

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  • Author: Sebastian Raschka
  • Publisher: Packt Publishing Ltd
  • ISBN: 1783555149
  • Category : Computers
  • Languages : en
  • Pages : 455

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.

The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies

2014-01-20
The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies

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  • Author: Erik Brynjolfsson
  • Publisher: W. W. Norton & Company
  • ISBN: 0393239357
  • Category : Business & Economics
  • Languages : en
  • Pages : 320

A pair of technology experts describe how humans will have to keep pace with machines in order to become prosperous in the future and identify strategies and policies for business and individuals to use to combine digital processing power with human ingenuity.