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 classificat...

If you’re an experienced programmer willing to crunch data, this concise guide will show you how to use machine learning to work with email. You’ll learn how to write algorithms that automatically sort and redirect email based on statistical patterns. Authors Drew Conway and John Myles White approach the process in a practical fashion, using a case-study driven approach rather than a traditional math-heavy presentation.This book also includes a short tutorial on using the popular R...

No matter how much you've aged or how confident you may be in the dark, your fear will continue to grow roots in your mind. Strangest Fiction is back with the third installment of our namesake speculative fiction series. We've prepared a collection of bad dreams to haunt you, dear reader. You can't escape what's coming, no matter if it's a possessed doll, a ravenous swamp, or a flesh-eating creature from the deep. We hope you enjoy these intense tales from our talented storytellers. Stay s...

If you were dreaming right now, would you realize it? Between surreal creatures, endless nightmares, and violence that may not be what it seems, this anthology blurs the line between reality and fantasy.Featuring stories by Sirius, Nicole Duffeck, Aaron Lebold, Alice Lorentino, P.N. Harrison, Andrew Darlington, Charles Stott, Justin Samuels, Fariel Shafee, Zazie Kanwar-Torge, A.R.I. Hemmings, Wendy Wheeler, Paul Lewthwaite, Jack Feerick, J.G. McGrath, Holly Horror, Nenad Mitrovich,...

Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain...

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics ...

Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share n...

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 ...

Building a simple but powerful recommendation system is much easier than you think. Approachable for all levels of expertise, this report explains innovations that make machine learning practical for business production settings—and demonstrates how even a small-scale development team can design an effective large-scale recommendation system.Apache Mahout committers Ted Dunning and Ellen Friedman walk you through a design that relies on careful simplification. You’ll learn how to c...

Essential Information about Algorithms and Data StructuresA Classic ReferenceThe latest version of Sedgewick’s best-selling series, reflecting an indispensable body of knowledge developed over the past several decades.Broad CoverageFull treatment of data structures and algorithms for sorting, searching, graph processing, and string processing, including fifty algorithms every programmer should know. See algs4.cs.p...

“Bruce Schneier’s amazing book is the best overview of privacy and security ever written.”—Clay ShirkyYour cell phone provider tracks your location and knows who’s with you. Your online and in-store purchasing patterns are recorded, and reveal if you're unemployed, sick, or pregnant. Your e-mails and texts expose your intimate and casual friends. Google knows what you’re thinking because it saves your private searches. Facebook can determine your sexual orientation...

This book constitutes the refereed proceedings of the 12th Latin American Symposium on Theoretical Informatics, LATIN 2016, held in Ensenada, Mexico, in April 2016.The 52 papers presented together with 5 abstracts were carefully reviewed and selected from 131 submissions. The papers address a variety of topics in theoretical computer science with a certain focus on algorithms (approximation, online, randomized, algorithmic game theory, etc.), analytic combinatorics and analysis of a...