{"product_id":"probabilistic-machine-learning-isbn-9780262048439","title":"Probabilistic Machine Learning","description":"\u003cb\u003eAn advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, generative models, and decision making under uncertainty.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eAn advanced counterpart to \u003ci\u003eProbabilistic Machine Learning: An Introduction,\u003c\/i\u003e this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts deep learning into a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference. With contributions from top scientists and domain experts from places such as Google, DeepMind, Amazon, Purdue University, NYU, and the University of Washington, this rigorous book is essential to understanding the vital issues in machine learning.\u003cbr\u003e\u003cbr\u003e\u003cul\u003e\n\u003cli\u003eCovers generation of high dimensional outputs, such as images, text, and graphs \u003c\/li\u003e\n\u003cli\u003eDiscusses methods for discovering insights about data, based on latent variable models \u003c\/li\u003e\n\u003cli\u003eConsiders training and testing under different distributions\u003c\/li\u003e\n\u003cli\u003eExplores how to use probabilistic models and inference for causal inference and decision making\u003c\/li\u003e\n\u003cli\u003eFeatures online Python code accompaniment \u003c\/li\u003e\n\u003c\/ul\u003e1 Introduction 1\u003cbr\u003eI Fundamentals 3\u003cbr\u003e2 Probability 5\u003cbr\u003e3 Statistics 63\u003cbr\u003e4 Graphical models 143\u003cbr\u003e5 Information theory 217\u003cbr\u003e6 Optimization 255\u003cbr\u003eII Inference 337\u003cbr\u003e7 Inference algorithms: an overview 339\u003cbr\u003e8 Gaussian filtering and smoothing 353\u003cbr\u003e9 Message passing algorithms 395\u003cbr\u003e10 Variational inference 433\u003cbr\u003e11 Monte Carlo methods 477\u003cbr\u003e12 Markov chain Monte Carlo 493\u003cbr\u003e13 Sequential Monte Carlo 537\u003cbr\u003eIII Prediction 567\u003cbr\u003e14 Predictive models: an overview 569\u003cbr\u003e15 Generalized linear models 583\u003cbr\u003e16 Deep neural networks 623\u003cbr\u003e17 Bayesian neural networks 639\u003cbr\u003e18 Gaussian processes 673\u003cbr\u003e19 Beyond the iid assumption 727\u003cbr\u003eIV Generation 763\u003cbr\u003e20 Generative models: an overview 765\u003cbr\u003e21 Variational autoencoders 781\u003cbr\u003e22 Autoregressive models 811\u003cbr\u003e23 Normalizing flows 819\u003cbr\u003e24 Energy-based models 839\u003cbr\u003e25 Diffusion models 857\u003cbr\u003e26 Generative adversarial networks 883\u003cbr\u003eV Discovery 915\u003cbr\u003e27 Discovery methods: an overview 917\u003cbr\u003e28 Latent factor models 919\u003cbr\u003e29 State-space models 969\u003cbr\u003e30 Graph learning 1031\u003cbr\u003e31 Nonparametric Bayesian models 1035\u003cbr\u003e32 Representation learning 1037\u003cbr\u003e33 Interpretability 1061\u003cbr\u003eVI Action 1091\u003cbr\u003e34 Decision making under uncertainty 1093\u003cbr\u003e35 Reinforcement learning 1133\u003cbr\u003e36 Causality 1171Kevin P. Murphy is a Research Scientist at Google in Mountain View, California, where he works on artificial intelligence, machine learning, and Bayesian modeling.","brand":"The MIT Press","offers":[{"title":"Default Title","offer_id":46305112228069,"sku":"NP9780262048439","price":150.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780262048439.jpg?v=1767735144","url":"https:\/\/k12savings.com\/products\/probabilistic-machine-learning-isbn-9780262048439","provider":"K12savings","version":"1.0","type":"link"}