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This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

"This comprehensive, six-volume collection addresses all aspects of online and distance learning, including information communication technologies applied to education, virtual classrooms, pedagogical systems, Web-based learning, library information systems, virtual universities, and more. It enables libraries to provide a foundational reference to meet the information needs of researchers, educators, practitioners, administrators, and other stakeholders in online and distance learning"--Provided by publisher.

‘A superb study ... brilliant stories, hilarious observations and jaw dropping revelations about so many figures in public life we thought we knew – but never understood’ EMILY MAITLIS Loss and adversity are part of the human condition, but an imperfect past isn’t always an indicator of what’s to come.

George Washington ranks as one of the great military leaders in history. The character traits he exemplified, and the leadership skills he employed, enabled him to defeat what was then the world's most powerful nation. In this marvelous book, Mark McNeilly shows today's managers how they can learn from Washington's career--both his triumphs and setbacks--to succeed as leaders in their chosen field. McNeilly paints vivid portraits of some of the crucial moments in Washington's military career, from the early debacle on Long Island Heights to the masterstroke at Trenton. There Washington, aided by his use of intelligence and disinformation, and by his great fortitude in the face of truly daunting conditions, routed the Hessians. McNeilly uses these stirring military encounters to underscore Washington's managerial genius: to persuade and inspire, to open up the decision-making process, to seize opportunities when they arise, to persevere when setbacks occurred, and to learn from his mistakes. Indeed, the true value of the book lies in McNeilly's brilliant ability to link military and business strategy, revealing that successful corporate leaders must possess many of the same traits that Washington did. Using examples from the NFL, Cadillac, Coke, Samsung, Embraer, IBM and others, McNeilly shows how business leaders can apply Washington's principles for success. Blending colorful military and business history with crystal-clear commentary, George Washington and the Art of Business belongs of the shelves of all executives who want to hone their leadership skills.

The essential introduction to the principles and applications of feedback systems—now fully revised and expanded This textbook covers the mathematics needed to model, analyze, and design feedback systems. Now more user-friendly than ever, this revised and expanded edition of Feedback Systems is a one-volume resource for students and researchers in mathematics and engineering. It has applications across a range of disciplines that utilize feedback in physical, biological, information, and economic systems. Karl Åström and Richard Murray use techniques from physics, computer science, and operations research to introduce control-oriented modeling. They begin with state space tools for analysis and design, including stability of solutions, Lyapunov functions, reachability, state feedback observability, and estimators. The matrix exponential plays a central role in the analysis of linear control systems, allowing a concise development of many of the key concepts for this class of models. Åström and Murray then develop and explain tools in the frequency domain, including transfer functions, Nyquist analysis, PID control, frequency domain design, and robustness. Features a new chapter on design principles and tools, illustrating the types of problems that can be solved using feedback Includes a new chapter on fundamental limits and new material on the Routh-Hurwitz criterion and root locus plots Provides exercises at the end of every chapter Comes with an electronic solutions manual An ideal textbook for undergraduate and graduate students Indispensable for researchers seeking a self-contained resource on control theory

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering, resampling, deep learning and more. · Uses a hands-on approach and real world data.