Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

    The authors' enthusiasm for their subject is compelling as they present abstract concepts in a humorous and easy-to-grasp fashion. Together, these books will open new doors of thought to anyone who wants to find out what computing is really about. The Little Schemer introduces computing as an extension of arithmetic and algebra; things that everyone studies in grade school and high school. It introduces programs as recursive functions and briefly discusses the limits of what computers can do. The authors use the programming language Scheme, and interesting foods to illustrate these abstract ideas. The Seasoned Schemer informs the reader about additional dimensions of computing: functions as values, change of state, and exceptional cases. The Little LISPer has been a popular introduction to LISP for many years. It had appeared in French and Japanese. The Little Schemer and The Seasoned Schemer are worthy successors and will prove equally popular as textbooks for Scheme courses as well as companion texts for any complete introductory course in Computer Science.

    This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

    The book begins with the basic concepts of logic and set theory, to familiarize students with the language of mathematics and how it is interpreted. These concepts are used as the basis for a step-by-step breakdown of the most important techniques used in constructing proofs. To help students construct their own proofs, this new edition contains over 200 new exercises, selected solutions, and an introduction to Proof Designer software. No background beyond standard high school mathematics is assumed. Previous Edition Hb (1994) 0-521-44116-1 Previous Edition Pb (1994) 0-521-44663-5

    It also covers add-on utilities and how to take advantage of clever features in the most popular UNIX utilities.Loaded with even more practical advice about almost every aspect of UNIX, this edition addresses the technology that UNIX users face today, differing from the first edition in a number of important ways.First, it slants the blend of options and commands more toward the POSIX utilities, including the GNU versions; the bash and tcsh shells have greater coverage, but we've kept the first edition's emphasis on the core concepts of sh and csh that will help you use all UNIX shells; and, Perl is more important than awk these days, so we've de-emphasized awk in this edition.This is a browser's book...like a magazine that you don't read from start to finish, but leaf through repeatedly until you realize that you've read it all. The book is structured so that it bursts at the seams with cross references. Interesting "sidebars" explore syntax or point out other directions for exploration, including relevant technical details that might not be immediately apparent. You'll find articles abstracted from other O'Reilly books, new information that highlights program "tricks" and "gotchas," tips posted to the Net over the years, and other accumulated wisdom.The 53 chapters in this book discuss topics like file management, text editors, shell programming -- even office automation. Overall, there's plenty of material here to satisfy even the most voracious appetites. The bottom line? UNIX Power Tools is loaded with practical advice about almost every aspect of UNIX. It will help you think creatively about UNIX, and will help you get to the point where you can analyze your own problems. Your own solutions won't be far behind.The CD-ROM includes all of the scripts and aliases from the book, plus perl, GNU emacs, netpbm (graphics manipulation utilities), ispell,screen, the sc spreadsheet, and about 60 other freeware programs. In addition to the source code, all the software is precompiled for Sun4, Digital UNIX, IBM AIX, HP/UX, Red Hat Linux, Solaris, and SCO UNIX.

    Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you'll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data

    Featuring techniques not taught in any certification prep or covered by common defensive scanners, this book integrates social engineering, programming, and vulnerability exploits into a multidisciplinary approach for targeting and compromising high security environments. From discovering and creating attack vectors, and moving unseen through a target enterprise, to establishing command and exfiltrating data--even from organizations without a direct Internet connection--this guide contains the crucial techniques that provide a more accurate picture of your system's defense. Custom coding examples use VBA, Windows Scripting Host, C, Java, JavaScript, Flash, and more, with coverage of standard library applications and the use of scanning tools to bypass common defensive measures.Typical penetration testing consists of low-level hackers attacking a system with a list of known vulnerabilities, and defenders preventing those hacks using an equally well-known list of defensive scans. The professional hackers and nation states on the forefront of today's threats operate at a much more complex level--and this book shows you how to defend your high security network.Use targeted social engineering pretexts to create the initial compromise Leave a command and control structure in place for long-term access Escalate privilege and breach networks, operating systems, and trust structures Infiltrate further using harvested credentials while expanding control Today's threats are organized, professionally-run, and very much for-profit. Financial institutions, health care organizations, law enforcement, government agencies, and other high-value targets need to harden their IT infrastructure and human capital against targeted advanced attacks from motivated professionals. Advanced Penetration Testing goes beyond Kali linux and Metasploit and to provide you advanced pen testing for high security networks.

    A dirty little secret assembly language programmers rarely admit to, however, is that what you really need to learn is machine organization, not assembly language programming. Write Great Code Vol I, the first in a series from assembly language expert Randall Hyde, dives right into machine organization without the extra overhead of learning assembly language programming at the same time. And since Write Great Code Vol I concentrates on the machine organization, not assembly language, the reader will learn in greater depth those subjects that are language-independent and of concern to a high level language programmer. Write Great Code Vol I will help programmers make wiser choices with respect to programming statements and data types when writing software, no matter which language they use.

    Sipser's candid, crystal-clear style allows students at every level to understand and enjoy this field. His innovative "proof idea" sections explain profound concepts in plain English. The new edition incorporates many improvements students and professors have suggested over the years, and offers updated, classroom-tested problem sets at the end of each chapter.

    Algorithms in a Nutshell describes a large number of existing algorithms for solving a variety of problems, and helps you select and implement the right algorithm for your needs -- with just enough math to let you understand and analyze algorithm performance. With its focus on application, rather than theory, this book provides efficient code solutions in several programming languages that you can easily adapt to a specific project. Each major algorithm is presented in the style of a design pattern that includes information to help you understand why and when the algorithm is appropriate. With this book, you will:Solve a particular coding problem or improve on the performance of an existing solutionQuickly locate algorithms that relate to the problems you want to solve, and determine why a particular algorithm is the right one to useGet algorithmic solutions in C, C++, Java, and Ruby with implementation tipsLearn the expected performance of an algorithm, and the conditions it needs to perform at its bestDiscover the impact that similar design decisions have on different algorithmsLearn advanced data structures to improve the efficiency of algorithmsWith Algorithms in a Nutshell, you'll learn how to improve the performance of key algorithms essential for the success of your software applications.

    One slogan of Perl is that it makes easy things easy and hard things possible. "Intermediate Perl" is about making the leap from the easy things to the hard ones.Originally released in 2003 as "Learning Perl Objects, References, and Modules" and revised and updated for Perl 5.8, this book offers a gentle but thorough introduction to intermediate programming in Perl. Written by the authors of the best-selling "Learning Perl," it picks up where that book left off. Topics include: Packages and namespacesReferences and scopingManipulating complex data structuresObject-oriented programmingWriting and using modulesTesting Perl codeContributing to CPANFollowing the successful format of "Learning Perl," we designed each chapter in the book to be small enough to be read in just an hour or two, ending with a series of exercises to help you practice what you've learned. To use the book, you just need to be familiar with the material in "Learning Perl" and have ambition to go further.Perl is a different language to different people. It is a quick scripting tool for some, and a fully-featured object-oriented language for others. It is used for everything from performing quick global replacements on text files, to crunching huge, complex sets of scientific data that take weeks to process. Perl is what you make of it. But regardless of what you use Perl for, this book helps you do it more effectively, efficiently, and elegantly."Intermediate Perl" is about learning to use Perl as a programming language, and not just a scripting language. This is the book that turns the Perl dabbler into the Perl programmer.

    From creating new data-driven products through to increasing operational efficiency, big data has the potential to makeyour organization both more competitive and more innovative.As this emerging field transitions from the bleeding edge to enterprise infrastructure, it's vital to understand not only the technologies involved, but the organizational and cultural demands of being data-driven.Written by O'Reilly Radar's experts on big data, this anthology describes:- The broad industry changes heralded by the big data era- What big data is, what it means to your business, and how to start solving data problems- The software that makes up the Hadoop big data stack, and the major enterprise vendors' Hadoop solutions- The landscape of NoSQL databases and their relative merits- How visualization plays an important part in data work

      Statistics are everywhere, as integral to science as they are to business, and in the popular media hundreds of times a day. In this age of big data, a basic grasp of statistical literacy is more important than ever if we want to separate the fact from the fiction, the ostentatious embellishments from the raw evidence -- and even more so if we hope to participate in the future, rather than being simple bystanders. In The Art of Statistics, world-renowned statistician David Spiegelhalter shows readers how to derive knowledge from raw data by focusing on the concepts and connections behind the math. Drawing on real world examples to introduce complex issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether a notorious serial killer could have been caught earlier, and if screening for ovarian cancer is beneficial. The Art of Statistics not only shows us how mathematicians have used statistical science to solve these problems -- it teaches us how we too can think like statisticians. We learn how to clarify our questions, assumptions, and expectations when approaching a problem, and -- perhaps even more importantly -- we learn how to responsibly interpret the answers we receive. Combining the incomparable insight of an expert with the playful enthusiasm of an aficionado, The Art of Statistics is the definitive guide to stats that every modern person needs.

    

    Although its remarkable capabilities have made Ruby on Rails one of the world’s most popular web development frameworks, it can be challenging to learn and use. Ruby on Rails™ 3 Tutorial is the solution. Leading Rails developer Michael Hartl teaches Rails 3 by guiding you through the development of your own complete sample application using the latest techniques in Rails web development.Drawing on his experience building RailsSpace, Insoshi, and other sophisticated Rails applications, Hartl illuminates all facets of design and implementation—including powerful new techniques that simplify and accelerate development.You’ll find integrated tutorials not only for Rails, but also for the essential Ruby, HTML, CSS, JavaScript, and SQL skills you’ll need when developing web applications. Hartl explains how each new technique solves a real-world problem, and he demonstrates this with bite-sized code that’s simple enough to understand, yet novel enough to be useful. Whatever your previous web development experience, this book will guide you to true Rails mastery.This book will help you Install and set up your Rails development environment Go beyond generated code to truly understand how to build Rails applications from scratch Learn Test Driven Development (TDD) with RSpec Effectively use the Model-View-Controller (MVC) pattern Structure applications using the REST architecture Build static pages and transform them into dynamic ones Master the Ruby programming skills all Rails developers need Define high-quality site layouts and data models Implement registration and authentication systems, including validation and secure passwords Update, display, and delete users Add social features and microblogging, including an introduction to Ajax Record version changes with Git and share code at GitHub Simplify application deployment with Heroku