In Written in Bones, significant discoveries are carefully brought together and analyzed. Readers learn how experts use modern scientific techniques to piece together the stories behind the bones. The data is used to create a picture of cultures and ritual beliefs. There are such astonishing discoveries as:Han Dynasty aristocrat preserved in an unknown red liquid Bog bodies in Europe The riddle of Tomb KV55 - where a male body was found inside a female coffin World's oldest dwarf The headless men and giant wolves of the Mesolithic cemetery in Siberia

    Every new reader's brain possesses the extraordinary capacity to rearrange itself beyond its original abilities in order to understand written symbols. But how does the brain learn to read? As world-renowned cognitive neuroscientist and scholar of reading Maryanne Wolf explains in this impassioned book, we taught our brain to read only a few thousand years ago, and in the process changed the intellectual evolution of our species.Wolf tells us that the brain that examined tiny clay tablets in the cuneiform script of the Sumerians is configured differently from the brain that reads alphabets or of one literate in today's technology.There are critical implications to such an evolving brain. Just as writing reduced the need for memory, the proliferation of information and the particular requirements of digital culture may short-circuit some of written language's unique contributions—with potentially profound consequences for our future.Turning her attention to the development of the individual reading brain, Wolf draws on her expertise in dyslexia to investigate what happens when the brain finds it difficult to read. Interweaving her vast knowledge of neuroscience, psychology, literature, and linguistics, Wolf takes the reader from the brains of a pre-literate Homer to a literacy-ambivalent Plato, from an infant listening to Goodnight Moon to an expert reader of Proust, and finally to an often misunderstood child with dyslexia whose gifts may be as real as the challenges he or she faces.As we come to appreciate how the evolution and development of reading have changed the very arrangement of our brain and our intellectual life, we begin to realize with ever greater comprehension that we truly are what we read. Ambitious, provocative, and rich with examples, Proust and the Squid celebrates reading, one of the single most remarkable inventions in history. Once embarked on this magnificent story of the reading brain, you will never again take for granted your ability to absorb the written word.

    The year 2000 is alternatively the year 2544 (Buddhist), 6236 (Ancient Egyptian), 5761 (Jewish) or simply the Year of the Dragon (Chinese). The story of the creation of the Western calendar, which is related in this book, is a story of emperors and popes, mathematicians and monks, and the growth of scientific calculation to the point where, bizarrely, our measurement of time by atomic pulses is now more accurate than time itself: the Earth is an elderly lady and slightly eccentric - she loses half a second a century. Days have been invented (Julius Caesar needed an extra 80 days in 46BC), lost (Pope Gregory XIII ditched ten days in 1582) and moved (because Julius Caesar had 31 in his month, Augustus determined that he should have the same, so he pinched one from February).

    

    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.

    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.

    In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.

    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.