The paper, published in 1950 in Mind, was the first to introduce his concept of what is now known as the Turing test to the general public.Published in Mind 49: page 433-460.(Source: Wikipedia)

    Included are step-by-step instructions on how to carry out Bayesian data analyses.Download Link : readbux.com/download?i=0124058884            0124058884 Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan PDF by John Kruschke

    Select a topic for entirely strategic reasons. Choose the coolest supervisor. Write only to deadlines. Expect people to hold your hand. Become “that” student. When it comes to a masters or PhD program, most graduate students don’t deliberately set out to  fail. Yet, of the nearly 500,000 people who start a graduate program each year, up to half will never complete their degree. Books abound on acing the admissions process, but there is little on what to do once the acceptance letter arrives. Veteran graduate directors Kevin D. Haggerty and Aaron Doyle have set out to demystify the world of advanced education. Taking a wry, frank approach, they explain the common mistakes that can trip up a new graduate student and lay out practical advice about how to avoid the pitfalls. Along the way they relate stories from their decades of mentorship and even share some slip-ups from their own grad experiences. The litany of foul-ups is organized by theme and covers the grad school experience from beginning to end: selecting the university and program, interacting with advisors and fellow students, balancing personal and scholarly lives, navigating a thesis, and creating a life after academia. Although the tone is engagingly tongue-in-cheek, the lessons are crucial to anyone attending or contemplating grad school. 57 Ways to Screw Up in Grad School allows you to learn from others’ mistakes rather than making them yourself.

    Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge.Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data- including stream data, sequence data, graph structured data, social network data, and multi-relational data.A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business dataUpdates that incorporate input from readers, changes in the field, and more material on statistics and machine learningDozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projectsComplete classroom support for instructors at www.mkp.com/datamining2e companion site

    Python is a beautiful computer language. It is simple, and it is intuitive. Python is used by a sorts of people – data scientists use it for much of their number crunching and analytics; security testers use it for testing out security and IT attacks; it is used to develop high-quality web applications and many of the large applications that you use on the internet are also written in Python, including YouTube, DropBox, and Instagram. Are you interested in learning Python? Then settle in and learn the basics in just 7 days - enough for you to be comfortable in moving on to the next level without any trouble.Are you interested in learning Python? Then settle in and learn the basics in just 7 days - enough for you to be comfortable in moving on to the next level without any trouble. In this book you'll learn: Setting Up Your Environment Let’s Get Programming Variables and Programs in Files Loops, Loops and More Loops Functions Dictionaries, Lists, and Tuples The “for” Loop Classes Modules File Input/Output Error Handling and much more! Now it's time for you to start your journey into Python programming! Click on the Buy Now button above and get started today!

    This practical book shows you how to bundle reusable R functions, sample data, and documentation together by applying author Hadley Wickham’s package development philosophy. In the process, you’ll work with devtools, roxygen, and testthat, a set of R packages that automate common development tasks. Devtools encapsulates best practices that Hadley has learned from years of working with this programming language. Ideal for developers, data scientists, and programmers with various backgrounds, this book starts you with the basics and shows you how to improve your package writing over time. You’ll learn to focus on what you want your package to do, rather than think about package structure. Learn about the most useful components of an R package, including vignettes and unit tests Automate anything you can, taking advantage of the years of development experience embodied in devtools Get tips on good style, such as organizing functions into files Streamline your development process with devtools Learn the best way to submit your package to the Comprehensive R Archive Network (CRAN) Learn from a well-respected member of the R community who created 30 R packages, including ggplot2, dplyr, and tidyr

    Permutation city is the tale of a man with a vision - how to create immortality - and how that vision becomes something way beyond his control. Encompassing the lives and struggles of an artificial life junkie desperate to save her dying mother, a billionaire banker scarred by a terrible crime, the lovers for whom, in their timeless virtual world, love is not enough - and much more - Permutation city is filled with the sense of wonder.

    Reflecting the need for even minor programming in today's model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work.The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation.By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling.Web ResourceThe book is accompanied by an R package (rethinking) that is available on the author's website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.

    Engaging and accessible, Life on the Tenure Track will delight and enlighten faculty, graduate students, and administrators alike.

    Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.

    Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.

    It was the dawn of a new era in artificial intelligence: a machine capable of beating the reigning human champion at this most cerebral game. That moment was more than a century in the making, and in this breakthrough book, Kasparov reveals his astonishing side of the story for the first time. He describes how it felt to strategize against an implacable, untiring opponent with the whole world watching, and recounts the history of machine intelligence through the microcosm of chess, considered by generations of scientific pioneers to be a key to unlocking the secrets of human and machine cognition. Kasparov uses his unrivaled experience to look into the future of intelligent machines and sees it bright with possibility. As many critics decry artificial intelligence as a menace, particularly to human jobs, Kasparov shows how humanity can rise to new heights with the help of our most extraordinary creations, rather than fear them. Deep Thinking is a tightly argued case for technological progress, from the man who stood at its precipice with his own career at stake.

    It’s time we stand face-to-digital-face with the true powers and limitations of the algorithms that already automate important decisions in healthcare, transportation, crime, and commerce. Hello World is indispensable preparation for the moral quandaries of a world run by code, and with the unfailingly entertaining Hannah Fry as our guide, we’ll be discussing these issues long after the last page is turned.

    GRAPH THEORY WITH APPLICATIONS TO ENGINEERING AND COMPUTER SCIENCE-PHI-DEO, NARSINGH-1979-EDN-1

    We currently see no evidence of any kind indicating that extraterrestrials exist outside of our solar system. But at this moment, millions of engineers, scientists, corporations, universities and entrepreneurs are racing to create the second intelligent species right here on planet earth. And we can see the second intelligent species coming from all directions in the form of self-driving cars, automated call centers, chess-playing and Jeopardy-playing computers that beat all human players, airport kiosks, restaurant tablet systems, etc. The frightening thing is that these robots will soon be eliminating human jobs in startling numbers. The first wave of unemployed workers is likely to be a million truck drivers who are replaced by self-driving trucks. Pilots will be eliminated soon as well. Then, as new computer vision systems come online, we will see tens of millions of workers in retail stores, fast food restaurants and construction sites replaced by robots. Unless we take steps now to change the economy, we will soon have tens of millions of workers who are unemployed and seeking welfare because they will have no other choice. Marshall Brain's new book "The Second Intelligent Species: How Humans Will Become as Irrelevant as Cockroaches" explores how the future will unfold as the second intelligent species emerges. The book answers questions like: - How will new computer vision systems affect the job market? - How many people will become unemployed by the second intelligent species? - What will happen to millions of newly unemployed workers? - How can modern society and modern economies cope with run-away unemployment caused by robots? - What will happen when the first sentient, conscious computer appears? - What moral and ethical principles will guide the second intelligent species? - Why do we see no extraterrestrials in our universe? "The Second Intelligent Species" offers a unique and fascinating look at the future of the human race, and the choices we will need to make to avoid massive unemployment and poverty worldwide as intelligent machines start eliminating millions of jobs.