A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.
Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.
The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package―PMTK (probabilistic modeling toolkit)―that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)

Computers & Technology

Murphy, Kevin P.

Computer Science

The MIT Press

A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This 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. Probabilistic Machine Learning grew out of the author's 2012 book, Machine Learning- A Probabilistic Perspective. 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.

Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)

An 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.\nAn advanced counterpart to Probabilistic Machine Learning: An Introduction, 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.\nCovers generation of high dimensional outputs, such as images, text, and graphs Discusses methods for discovering insights about data, based on latent variable models Considers training and testing under different distributions Explores how to use probabilistic models and inference for causal inference and decision making Features online Python code accompaniment

Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning series)

In this special issue of Radical History Review, scholars and activists examine the rise of “homonormativity,” a lesbian and gay politics that embraces neoliberal values under the guise of queer sexual liberation. Contributors look at the historical forces through which lesbian and gay rights organizations and community advocates align with social conservatives and endorse family-oriented formations associated with domestic partnership, adoption, military service, and gender-normative social roles. 
Distinguished by its historical approach, “Queer Futures” examines homonormativity as a phenomenon that emerged in the United States after World War II and gained traction in the 1960s and 1970s. One essay compares Anita Bryant’s antigay campaigns in the late 1970s with those of current same-sex marriage proponents to show how both focus on the abstract figure of the “endangered child.” Another essay explores how the Gay and Lesbian Alliance Against Defamation’s organizational amnesia has shaped its often conservative agenda. Other essays include a Marxist reading of the transsexual body, an examination of reactionary politics at the core of the movement to repeal the U.S. military’s “don’t ask, don’t tell” policy, and a history of how “safe streets” patrols in the 1970s and 1980s became opportunities for urban gentrification and community exploitation. 
Contributors. Anna M. Agathangelou, Daniel Bassichis, Aaron Belkin, Nan Alamilla Boyd, Maxime Cervulle, Vincent Doyle, Roderick A. Ferguson, Christina Hanhardt, Dan Irving, Regina Kunzel, Patrick McCreery, Kevin P. Murphy, Tavia Nyong’o, Jason Ruiz, David Serlin, Tamara L. Spira, Susan Stryker, Margot D. Weiss

Queer Futures (Volume 2008) (Radical History Review (Duke University Press))

History

Ruiz, Jason

Serlin, David

Historical Study & Educational Resources

Duke University Press Books

See the best price to sell, buy, or rent books by Murphy, Kevin P.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Murphy, Kevin P.

See the best price to sell, buy, or rent books by Murphy, Kevin P.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)

Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)

Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning series)

Queer Futures (Volume 2008) (Radical History Review (Duke University Press))