Programming Collective Intelligence

Author: Toby Segaran
Publisher: "O'Reilly Media, Inc."
ISBN: 0596550685
Format: PDF, ePub, Mobi
Download Now
Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains: Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details." -- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths." -- Tim Wolters, CTO, Collective Intellect

Programming Collective Intelligence

Author: Toby Segaran
Publisher: "O'Reilly Media, Inc."
ISBN: 0596517602
Format: PDF, Docs
Download Now
Provides information on building Web 2.0 applications that have the capability to mine data created by Internet applications.

Collective Intelligence in Action

Author: Satnam Alag
Publisher: Manning Publications
ISBN: 9781933988313
Format: PDF, Docs
Download Now
Provides information on using a Java-based CI toolkit to mine information to build more effective Web sites.

Machine Learning for Hackers

Author: Drew Conway
Publisher: "O'Reilly Media, Inc."
ISBN: 1449330533
Format: PDF, ePub, Docs
Download Now
If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation. Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research. Develop a naïve Bayesian classifier to determine if an email is spam, based only on its text Use linear regression to predict the number of page views for the top 1,000 websites Learn optimization techniques by attempting to break a simple letter cipher Compare and contrast U.S. Senators statistically, based on their voting records Build a “whom to follow” recommendation system from Twitter data

Beautiful Data

Author: Toby Segaran
Publisher: "O'Reilly Media, Inc."
ISBN: 144937929X
Format: PDF, ePub
Download Now
In this insightful book, you'll learn from the best data practitioners in the field just how wide-ranging -- and beautiful -- working with data can be. Join 39 contributors as they explain how they developed simple and elegant solutions on projects ranging from the Mars lander to a Radiohead video. With Beautiful Data, you will: Explore the opportunities and challenges involved in working with the vast number of datasets made available by the Web Learn how to visualize trends in urban crime, using maps and data mashups Discover the challenges of designing a data processing system that works within the constraints of space travel Learn how crowdsourcing and transparency have combined to advance the state of drug research Understand how new data can automatically trigger alerts when it matches or overlaps pre-existing data Learn about the massive infrastructure required to create, capture, and process DNA data That's only small sample of what you'll find in Beautiful Data. For anyone who handles data, this is a truly fascinating book. Contributors include: Nathan Yau Jonathan Follett and Matt Holm J.M. Hughes Raghu Ramakrishnan, Brian Cooper, and Utkarsh Srivastava Jeff Hammerbacher Jason Dykes and Jo Wood Jeff Jonas and Lisa Sokol Jud Valeski Alon Halevy and Jayant Madhavan Aaron Koblin with Valdean Klump Michal Migurski Jeff Heer Coco Krumme Peter Norvig Matt Wood and Ben Blackburne Jean-Claude Bradley, Rajarshi Guha, Andrew Lang, Pierre Lindenbaum, Cameron Neylon, Antony Williams, and Egon Willighagen Lukas Biewald and Brendan O'Connor Hadley Wickham, Deborah Swayne, and David Poole Andrew Gelman, Jonathan P. Kastellec, and Yair Ghitza Toby Segaran

Mining the Social Web

Author: Matthew A. Russell
Publisher: "O'Reilly Media, Inc."
ISBN: 1449388345
Format: PDF
Download Now
Provides information on data analysis from a vareity of social networking sites, including Facebook, Twitter, and LinkedIn.

Working with Preferences Less Is More

Author: Souhila Kaci
Publisher: Springer Science & Business Media
ISBN: 9783642172809
Format: PDF, Mobi
Download Now
Preferences are useful in many real-life problems, guiding human decision making from early childhood up to complex professional and organizational decisions. In artificial intelligence specifically, preferences is a relatively new topic of relevance to nonmonotonic reasoning, multiagent systems, constraint satisfaction, decision making, social choice theory and decision-theoretic planning The first part of this book deals with preference representation, with specific chapters dedicated to representation languages, nonmonotonic logics of preferences, conditional preference networks, positive and negative preferences, and the study of preferences in cognitive psychology. The second part of the book deals with reasoning with preferences, and includes chapters dedicated to preference-based argumentation, preferences database queries, and rank-ordering outcomes and intervals. The author concludes by examining forthcoming research perspectives. This is inherently a multidisciplinary topic and this book will be of interest to computer scientists, economists, operations researchers, mathematicians, logicians, philosophers and psychologists.

Big Mind

Author: Geoff Mulgan
Publisher: Princeton University Press
ISBN: 1400888514
Format: PDF, Kindle
Download Now
A new field of collective intelligence has emerged in the last few years, prompted by a wave of digital technologies that make it possible for organizations and societies to think at large scale. This “bigger mind”—human and machine capabilities working together—has the potential to solve the great challenges of our time. So why do smart technologies not automatically lead to smart results? Gathering insights from diverse fields, including philosophy, computer science, and biology, Big Mind reveals how collective intelligence can guide corporations, governments, universities, and societies to make the most of human brains and digital technologies. Geoff Mulgan explores how collective intelligence has to be consciously organized and orchestrated in order to harness its powers. He looks at recent experiments mobilizing millions of people to solve problems, and at groundbreaking technology like Google Maps and Dove satellites. He also considers why organizations full of smart people and machines can make foolish mistakes—from investment banks losing billions to intelligence agencies misjudging geopolitical events—and shows how to avoid them. Highlighting differences between environments that stimulate intelligence and those that blunt it, Mulgan shows how human and machine intelligence could solve challenges in business, climate change, democracy, and public health. But for that to happen we’ll need radically new professions, institutions, and ways of thinking. Informed by the latest work on data, web platforms, and artificial intelligence, Big Mind shows how collective intelligence could help us survive and thrive.

Programming Interactivity

Author: Joshua Noble
Publisher: "O'Reilly Media, Inc."
ISBN: 144931144X
Format: PDF, ePub
Download Now
Looks at the techniques of interactive design, covering such topics as 2D and 3D graphics, sound, computer vision, and geolocation.

Handbook of Collective Intelligence

Author: Thomas W. Malone
Publisher: MIT Press
ISBN: 0262331470
Format: PDF, Mobi
Download Now
Intelligence does not arise only in individual brains; it also arises in groups of individuals. This is collective intelligence: groups of individuals acting collectively in ways that seem intelligent. In recent years, a new kind of collective intelligence has emerged: interconnected groups of people and computers, collectively doing intelligent things. Today these groups are engaged in tasks that range from writing software to predicting the results of presidential elections. This volume reports on the latest research in the study of collective intelligence, laying out a shared set of research challenges from a variety of disciplinary and methodological perspectives. Taken together, these essays -- by leading researchers from such fields as computer science, biology, economics, and psychology -- lay the foundation for a new multidisciplinary field. Each essay describes the work on collective intelligence in a particular discipline -- for example, economics and the study of markets; biology and research on emergent behavior in ant colonies; human-computer interaction and artificial intelligence; and cognitive psychology and the "wisdom of crowds" effect. Other areas in social science covered include social psychology, organizational theory, law, and communications. ContributorsEytan Adar, Ishani Aggarwal, Yochai Benkler, Michael S. Bernstein, Jeffrey P. Bigham, Jonathan Bragg, Deborah M. Gordon, Benjamin Mako Hill, Christopher H. Lin, Andrew W. Lo, Thomas W. Malone, Mausam, Brent Miller, Aaron Shaw, Mark Steyvers, Daniel S. Weld, Anita Williams Woolley