Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.

    This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics--particularly in machine learning, scientific modeling, and artificial life--and reviews a broad span of research, including the work of Mitchell and her colleagues.The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting general purpose nature of genetic algorithms as search methods that can be employed across disciplines.An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.

    Every act can be categorized as either a selfish act or a selfless act. “Selfish or Selfless: Which One Are You?,” discusses how you can discover whether or not you are doing things that are selfish (about your own wants, your own need, and your own desires) or whether you are doing things that are selfless (things that are about other people’s wants, other people’s needs and you do things that benefit others). Do you know which one you are? Have you thought about why you do what you do and how it impacts the people around you? Learn how to discover whether you are selfish or selfless and how to change sides if you need to.

    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 naive Bayesian classifier to determine if an email is spam, based only on its textUse linear regression to predict the number of page views for the top 1,000 websitesLearn optimization techniques by attempting to break a simple letter cipherCompare and contrast U.S. Senators statistically, based on their voting recordsBuild a "whom to follow" recommendation system from Twitter data

    It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.

    Features include the latest information on the new Harry Potter attractions at Universal Studios as well as step-by-step touring plans that save four hours of waiting in line at Universal Studios and Universal's Island of Adventure. Complete chapters are devoted to the Universal parks, SeaWorld, Busch Gardens, Legoland, and the NASA Kennedy Space Center among others. Leading you step-by-step, it’s the guide that puts you ahead of the crowd and keeps you there.

    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

    I hope it helps you too!

    The book emphasizes a general introduction to probability theory and provides a detailed discussion of specific designs and analyses that are typically encountered in ecology and environmental science. Appropriate for use as either a stand-alone or supplementary text for upper-division undergraduate or graduate courses in ecological and environmental statistics, ecology, environmental science, environmental studies, or experimental design, the Primer also serves as a resource for environmental professionals who need to use and interpret statistics daily but have little or no formal training in the subject.

    Increasingly, the decisions that affect our lives--where we go to school, whether we can get a job or a loan, how much we pay for health insurance--are being made not by humans, but by machines. In theory, this should lead to greater fairness: Everyone is judged according to the same rules.But as mathematician and data scientist Cathy O'Neil reveals, the mathematical models being used today are unregulated and uncontestable, even when they're wrong. Most troubling, they reinforce discrimination--propping up the lucky, punishing the downtrodden, and undermining our democracy in the process.

    This highly anticipated fourth edition of the most ...Download Link : readmeaway.com/download?i=0128042915            0128042915 Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) PDF by Ian H. WittenRead Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) PDF from Morgan Kaufmann,Ian H. WittenDownload Ian H. Witten's PDF E-book Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems)

    This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory.The book is divided into three parts. Part I discusses the relationship between sensory stimuli and neural responses, focusing on the representation of information by the spiking activity of neurons. Part II discusses the modeling of neurons and neural circuits on the basis of cellular and synaptic biophysics. Part III analyzes the role of plasticity in development and learning. An appendix covers the mathematical methods used, and exercises are available on the book's Web site.

    Republished in book form shortly thereafter, it has since gone through four hardcover and sixteen paperback printings. It is a revolutionary work, astounding in its foresight and contemporaneity. The University of Illinois Press is pleased and honored to issue this commemorative reprinting of a classic.

    The Devil and Dr. Barnes traces the near-mythical journey of a man who was born into poverty, amassed a fortune through the promotion of a popular medicine, and acquired the premier private collection of works by such masters as Renoir, Matisse, Cézanne, and Picasso. Ostentatiously turning his back on the art establishment, Barnes challenged the aesthetic sensibilities of an uninitiated, often resistant and scoffing, American audience. In particular, he championed Matisse, Soutine, and Modigliani when they were obscure or in difficult straits. Analyzing what he saw as the formal relationships underlying all art, linking the old and the new, Barnes applied these principles in a rigorous course of study offered at his Merion foundation. Barnes's own mordant words, culled from the copious printed record, animate the narrative throughout, as do accounts of his associations with notables of the era--Gertrude and Leo Stein, Bertrand Russell, and John Dewey among them--many of whom he alienated with his appetite for passionate, public feuds. In this rounded portrait, Albert Barnes emerges as a complex, flawed man, who--blessed with an astute eye for greatness--has left us an incomparable treasure, gathered in one place and unforgettable to all who have seen it.

    Sir Ernest Shackleton had carefully picked crew and a stout, well-outfitted ship, the Endurance. But he had no radio, the world was at war, and at the edge of the Antarctic continent, the ship froze in the sea ice. After months of immobility, it was crushed. Then began an impossible journey. With three tiny boats, the crew worked their way across frozen the Antarctic Sea. This vivid book recounts the story of Shackleton's heroic voyage from South Georgia Island to Antarctica then back to South Georgia. It is a tribute to Shackleton and his crew's ability to fight for survival and one of the most harrowing adventures in history.