.atwr-toast-cover{position:fixed;left:0;top:0;z-index:10000;width:100%;height:100%;background-color:#FFF;opacity:.5}#atwrToastCover{display:none}.atwr-toast-container{position:fixed;left:50%;z-index:10001;top:50%}.atwr-message-box{font-size:16px;color:#fff;font-weight:700;font-family:arial;-webkit-border-radius:10px;border-radius:10px;background-color:#444c55;position:relative;left:-50%;top:0;text-align:center;margin:auto;opacity:1}.atwr-message-box-size{padding:0}.atwr-message-box-size-spinner{transition-property:padding;transition-duration:.2s;-webkit-transition-property:padding;-webkit-transition-duration:.2s}#atwrFailure,#atwrSuccess{display:none}.atwr-spinner-progress{height:45px;width:89px;background:url(https://images-na.ssl-images-amazon.com/images/G/01/udp/mobile/buy-box/atc-spinner.gif) 50% 50% no-repeat}#viewMyBabyRegistryButtonStack{display:none} The book has five parts, on inference in probabilistic models, learning in probabilistic models, machine learning, dynamical models, and approximate inference. They play an important role in a vast range of areas from game development to drug discovery. (window.AmazonUIPageJS ? Bayesian Reasoning and Machine Learning. #multiple-dfl-offers{border-radius:.3rem;border-color:#adb1b8 #a2a6ac #8d9096;border-style:solid;border-width:.1rem;position:relative;width:100%;padding:1rem 2.7rem 1rem 1rem}#multiple-dfl-offers .dfl-arrow{position:absolute;top:50%;right:16px;margin-top:-5.5px}#multiple-dfl-offers .currencyINR{background-position-x:0}#multiple-dfl-offers .dfl-widget.dfl-primary-peak-offer{height:60px}.dfl-primary-peak-offer{margin-left:45px;margin-right:10px}.dfl-primary-peak-offer.dfl-offer-content{height:40px;overflow:hidden}.dfl-special-offer{color:#b12704;font-weight:700}#dfl-more-offers{color:#0066c0;white-space:nowrap}.dfl-offer-footer{color:#0066c0;white-space:nowrap} this is not a book about machine learning in general (no svm for example) but it is a very readable introduction to.the Bayesian approach. Please try again. Published April 1st 2012 by Cambridge University Press (first published January 31st 2012. }); Would recommend the middle part of the book as a good, but slightly unorthodox introduction to machine learning. AmazonUIPageJS : P).when('atf').execute(function(){ Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems About This Book Understand the principles of Bayesian Inference with less mathematical equationsLearn state-of-the art Machine Learning methodsFamiliarize yourself with the recent advances in Deep Learning and Big Data frameworks with this step-by-step guide I liked the emphasis on missing values of some of the chapters. .tradein-instant-savings-launch-wrapper{margin-bottom:22px}.tradein-instant-savings-launch-button{overflow:hidden;border:1px solid #009502!important;background-image:linear-gradient(#fff,#f7f8fa 1px,#e7e9ec);box-shadow:0 1px rgba(0,0,0,.125);width:100%;margin:0!important}.tradein-instant-savings-launch-button .a-button-text{white-space:normal;text-align:left!important;color:#000!important;font-size:.9em;height:auto}.tradein-instant-savings-launch-button .a-button-inner{background:0 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