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Probabilistic Machine Learning
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...
Machine Learning
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 developm...
Machine Learning, second edition
The second and expanded edition of a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, including deep learning, viewed through the lens of probabilistic modeling and Bayesian decision theory. This second edition has been substantially expanded and revised, incorporating many recent developments in the field. It has new chapters on linear algebra, optimization, implicit generative models, reinforcement learning, and causality; and other chapters on such topics as variational inference and graphical models have been significantly updated. The software for the book (hosted on github) is now implemented in Python rather than MATLAB, and uses state-of-the-art libraries including as scikit-learn, Tensorflow 2, and JAX.
Validity Generalization
This volume presents the first wide-ranging critical review of validity generalization (VG)--a method that has dominated the field since the publication of Schmidt and Hunter's (1977) paper "Development of a General Solution to the Problem of Validity Generalization." This paper and the work that followed had a profound impact on the science and practice of applied psychology. The research suggests that fundamental relationships among tests and criteria, and the constructs they represent are simpler and more regular than they appear. Looking at the history of the VG model and its impact on personnel psychology, top scholars and leading researchers of the field review the accomplishments of the model, as well as the continuing controversies. Several chapters significantly extend the maximum likelihood estimation with existing models for meta analysis and VG. Reviewing 25 years of progress in the field, this volume shows how the model can be extended and applied to new problems and domains. This book will be important to researchers and graduate students in the areas of industrial organizational psychology and statistics.
Ecosystems
Gordon Dickinson and Kevin Murphy introduce the basic concepts and processes in the ecosystem, and explore its role in solving environmental problems.
Historicising Gender and Sexuality
Historicising Gender and Sexuality features a diverse collection of essays that shed new light on the historical intersections between gender and sexuality across time and space. Demonstrates both the particularities of specific formulations of gender and sexuality and the nature of the relationship between the categories themselves Presents evidence that careful and contextualised analysis of the shifting relationship of gender and sexuality illuminates broader historical processes
Probabilistic Machine Learning
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. An 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...
Inside the Bataan Death March
For two weeks during the spring of 1942, the Bataan Death March--one of the most widely condemned atrocities of World War II--unfolded. The prevailing interpretation of this event is simple: American prisoners of war suffered cruel treatment at the hands of their Japanese captors while Filipinos, sympathetic to the Americans, looked on. Most survivors of the march wrote about their experiences decades after the war and a number of factors distorted their accounts. The crucial aspect of memory is central to this study--how it is constructed, by whom and for what purpose. This book questions the prevailing interpretation, reconsiders the actions of all three groups in their cultural contexts and suggests a far greater complexity. Among the conclusions is that violence on the march was largely the result of a clash of cultures--undisciplined, individualistic Americans encountered Japanese who valued order and form, while Filipinos were active, even ambitious, participants in the drama.
Bayesian Reasoning and Machine Learning
A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.
Probabilistic Graphical Models
A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions. Most tasks require a person or an automated system to reason—to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because u...
