All of the mentioned are just puppets on an invisible string doing the biddings of a few unseen puppeteers. Yes, that’s right. A few elite and undisclosed organizations send our children off to war, restrict the growth of the middle class, and limit educational opportunities for American citizens. The sad truth is this is nothing new. Thomas Jefferson and Benjamin Franklin warned of the dangers and destructive power of these elites if left unchecked. These few unchosen were able, and continue, to use the Federal Reserve Banking System, universities, and war to create economic recessions and depressions that provide unnoticed benefits to a select group of social manipulators. In this stunning new book, Mark Mullen takes us on an intellectual journey through the world of secret partnerships created by unfamiliar ideologues designed to acquire most of the nation’s wealth and power. In Who Controls America, Mullen shines a light on those few elites who place greed, power, and profits above the interests of the American citizen and the pursuit of the American Dream.

    Terrorism spills out of the Middle East into Europe. Russia, Iran, Saudi Arabia, China and Japan vie to see who can be most aggressive. Financial breakdown in Asia and Europe guts growth, challenging hard-won political stability.Yet for the Americans, these changes are fantastic. Alone among the world's powers, only the United States is geographically wealthy, demographically robust, and energy secure. That last piece -- American energy security -- is rapidly emerging as the most critical piece of the global picture.The American shale revolution does more than sever the largest of the remaining ties that bind America's fate to the wider world. It re-industrializes the United States, accelerates the global order's breakdown, and triggers a series of wide ranging military conflicts that will shape the next two decades. The common theme? Just as the global economy tips into chaos, just as global energy becomes dangerous, just as the world really needs the Americans to be engaged, the United States will be...absent.In 2014's The Accidental Superpower, geopolitical strategist Peter Zeihan made the case that geographic, demographic and energy trends were unravelling the global system. Zeihan takes the story a step further in The Absent Superpower, mapping out the threats and opportunities as the world descends into Disorder.

    Together the chapters illustrate how economic theory and rigorous empirical analysis can shed light on some of the most important issues in social science and public policy namely, under what circumstances individuals break the law and how sanctions can be structured to most effectively prevent such behavior. This book will be an excellent resource for graduate students and researchers not only in economics, but in other social sciences as well. Brian A. Jacob, Harvard University, US This is a superb collection of one of the most important literatures in law and economics. The editors, two of the most productive and gifted scholars in this area, not only show the important historical evolution of the theoretical issues stemming from the seminal article by Gary Becker, but they also give a survey of the leading empirical works on the most salient issues in criminal justice. The editors introduction is a deft summary of one of the most significant contributions that economic analysis has made to the study of law. Thomas S. Ulen, University of Illinois, Urbana-Champaign, US The volume presents the seminal articles in the economic analysis of the criminal law. The articles include the path-breaking theoretical economic analyses of criminal behavior and the leading empirical tests of these theories. The volume also contains the most prominent economic analyses of the substantive doctrines of criminal law and criminal procedure. Other articles present influential applications of economic concepts and evidence to perennial issues in criminal law and criminal justice, such as gun control, drug prohibition, and sentencing policy. An introduction by the volume editors provides a comprehensive overview of the works included. Economics of Criminal Law will be an essential source of reference for scholars, graduate students in both law and in economics, and practitioners.

    Provides a review of introductory noncalculus-based physics for those who do not have a strong background in mathematics.

    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.

    Therefore, this book provides the fundamental concepts and techniques of real analysis for readers in all of these areas. It helps one develop the ability to think deductively, analyze mathematical situations and extend ideas to a new context. Like the first two editions, this edition maintains the same spirit and user-friendly approach with some streamlined arguments, a few new examples, rearranged topics, and a new chapter on the Generalized Riemann Integral.

    In this unconventional introduction, physicist Leonard Susskind and hacker-scientist George Hrabovsky offer a first course in physics and associated math for the ardent amateur. Unlike most popular physics books—which give readers a taste of what physicists know but shy away from equations or math—Susskind and Hrabovsky actually teach the skills you need to do physics, beginning with classical mechanics, yourself. Based on Susskind's enormously popular Stanford University-based (and YouTube-featured) continuing-education course, the authors cover the minimum—the theoretical minimum of the title—that readers need to master to study more advanced topics.An alternative to the conventional go-to-college method, The Theoretical Minimum provides a tool kit for amateur scientists to learn physics at their own pace.

    This text provides a simple and straightforward introduction to econometrics for the beginner. The book is designed to help students understand econometric techniques through extensive examples, careful explanations, and a wide variety of problem material. In each of the editions, I have tried to incorporate major developments in the field in an intuitive and informative way without resort to matrix algebra, calculus, or statistics beyond the introductory level. The fourth edition continues that tradition.

    It avoids overburdening the reader with topographical detail that is unnecessary for the medical student. Minimum assumptions are made of existing knowledge of the subject.'Key point' boxes for reinforcement and quick revision Glossary of important terms 'Clinical detail' boxes closely integrated with relevant neuroanatomyComplete revision and updating of text. Revision nad expansion of summary chapter, providing overview of entire subject. Clinical material updated to reflect current prevalence of neurological disease. Artwork entirely redrawn for improved clarity and closer integration with text.

    This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

    A step-by-step visual journey through the mathematics of neural networks, and making your own using Python and Tensorflow.

    The authors' comprehensive experience in legal practice, banking and teaching have enabled them to provide a condensed and easy to understand coverage of business law principles and areas of interest related thereto.

    In this, the fourth edition, Jonathan Baron retains the comprehensive attention to the key questions addressed in the previous editions - How should we think? What, if anything, keeps us from thinking that way? How can we improve our thinking and decision making? - and his expanded treatment of topics such as risk, utilitarianism, Baye's theorem, and moral thinking. With the student in mind, the fourth edition emphasizes the development of an understanding of the fundamental concepts in judgment and decision making. This book is essential reading for students and scholars in judgment and decision making and related fields, including psychology, economics, law, medicine, and business.

    Everything around us, including matter, is energy. A deep look into the mysteries of the subatomic world – the particles that make up the atom – provides answers to basic questions about how the universe works. To solve the future of mankind’s energy needs we need to understand the basic building blocks of the universe, including the atom and its parts. By exploring the subatomic world we’ll find more answers to our questions about time, forces like gravity and the matter that surrounds us. More importantly, we’ll find new ways to tap into the energy that exists around us to power our growing needs. In a new branch of particle physics, where tiny particles are thought of as energy waves, we find new answers that may help us in our quest to find alternative energy sources.

    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 uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.