{"product_id":"probabilistic-machine-learning-isbn-9780262046824","title":"Probabilistic Machine Learning","description":"\u003cb\u003eA detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eThis book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation.\u003cbr\u003e \u003cbr\u003e\u003ci\u003eProbabilistic Machine Learning\u003c\/i\u003e grew out of the author’s 2012 book, \u003ci\u003eMachine Learning: A Probabilistic Perspective\u003c\/i\u003e. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.1 Introduction 1\u003cbr\u003eI Foundations 29\u003cbr\u003e2 Probability: Univariate Models 31\u003cbr\u003e3 Probability: Multivariate Models 75\u003cbr\u003e4 statistics 103\u003cbr\u003e5 Decision Theory 163\u003cbr\u003e6 Information Theory 199\u003cbr\u003e7 Linear Algebra 221\u003cbr\u003e8 Optimization 269\u003cbr\u003eII Linear Models 315\u003cbr\u003e9 Linear Discriminant Analysis 317\u003cbr\u003e10 Logistic Regression 333\u003cbr\u003e11 Linear Regression 365\u003cbr\u003e12 Generalized Linear Models * 409\u003cbr\u003eIII Deep Neural Networks 417\u003cbr\u003e13 Neural Networks for Structured Data 419\u003cbr\u003e14 Neural Networks for Images 461\u003cbr\u003e15 Neural Networks for Sequences 497\u003cbr\u003eIV Nonparametric Models 539\u003cbr\u003e16 Exemplar-based Methods 541\u003cbr\u003e17 Kernel Methods * 561\u003cbr\u003e18 Trees, Forests, Bagging, and Boosting 597\u003cbr\u003eV Beyond Supervised Learning 619\u003cbr\u003e19 Learning with Fewer Labeled Examples 621\u003cbr\u003e20 Dimensionality Reduction 651\u003cbr\u003e21 Clustering 709\u003cbr\u003e22 Recommender Systems 735\u003cbr\u003e23 Graph Embeddings * 747\u003cbr\u003eA Notation 767“The deep learning revolution has transformed the field of machine learning over the last decade. It was inspired by attempts to mimic the way the brain learns but it is grounded in basic principles of statistics, information theory, decision theory, and optimization. This book does an excellent job of explaining these principles and describes many of the ‘classical’ machine learning methods that make use of them. It also shows how the same principles can be applied in deep learning systems that contain many layers of features. This provides a coherent framework in which one can understand the relationships and tradeoffs between many different ML approaches, both old and new.”\u003cbr\u003e—\u003cb\u003eGeoffrey Hinton, Emeritus Professor of Computer Science, University of Toronto; Engineering Fellow, Google\u003c\/b\u003e\u003cb\u003eKevin P. Murphy\u003c\/b\u003e is a Research Scientist at Google in Mountain View, California, where he works on AI, machine learning, computer vision, and natural language understanding. \u003cbr\u003e ","brand":"The MIT Press","offers":[{"title":"Default Title","offer_id":46303151554789,"sku":"NP9780262046824","price":125.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780262046824.jpg?v=1767735144","url":"https:\/\/k12savings.com\/es\/products\/probabilistic-machine-learning-isbn-9780262046824","provider":"K12savings","version":"1.0","type":"link"}