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Texas The Lone Star State

Crime and Criminal Justice in America

Electric and Electronic Circuit Simulation using TINA-TI

Mightier than the Sword How the News Media Have Shaped American History

Mightier than the Sword How the News Media Have Shaped American History

In this engaging examination of the media's influence on US history and politics Rodger Streitmatter visits sixteen landmark episodes from the American Revolution to the present-day fight for gay and lesbian marriage equality. In each of these cases Streitmatter succinctly illustrates the enormous role that journalism has played in not merely recording this nation's history but also in actively shaping it. Mightier than the Sword offers students and professors a highly readable and accessible alternative to journalism history textbooks. Instead of trying to document every detail in the development of US media through dry dull lists of names dates and headlines this book focuses on sixteen discrete episodes that illustrate a point that is much larger than the sum of their parts: media have played and continue to play an enormous role in shaping this nation. The fourth edition features an entirely new chapter on the way US media have championed various gay and lesbian rights initiatives from the 2003 Lawrence vs. Texas sodomy case through the June 2013 Supreme Court decision striking down DOMA (the Defense of Marriage Act). Balancing criticism and celebration of news media and exploring both print and electronic platforms Mightier than the Sword provides students with a sense of the power and responsibility inherent in the institution of journalism. | Mightier than the Sword How the News Media Have Shaped American History

GBP 130.00
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The Impact of Immigration on African Americans

The Impact of Immigration on African Americans

Immigration has significant consequences for all Americans but especially for African Americans. áThe sheer magnitude of immigration-it is the primary factor driving population growth-is so large that it directly or indirectly affects the economic political social and environmental circumstances of most Americans. áBut the geographic concentration of immigrants in urban areas and the economic concentration of immigrants in the low-wage sector of the labor market have special consequences for African Americans since they are especially likely to live in urban areas and to be low-wage workers. These effects can be both negative and positive. Immigration has sharply increased the supply of labor into the low-wage sector of the labor market which tends to reduce wages and employment opportunities for low-wage native workers. Employers may prefer hiring immigrants who are perceived to be hard working and uncomplaining to hiring African Americans. Immigrants can also increase the competition for scarce public services (especially education) on which African Americans depend. Yet immigration can also stimulate economic growth and urban revitalization which can increase job opportunities and spread an ideology of multiculturalism. Immigration can dilute the political power of African Americans but it can also strengthen the civil rights coalition. Immigration can benefit some groups while hurting others. This volume presents research and analysis that reflects and advances the debates about the economic and political consequences of immigration for African Americans. The contributors include Gerald Jaynes (Yale University) Vernon Briggs (Cornell University) Frank Bean and Jennifer Lee (University of California Irvine) Robert Cherry (Brooklyn College) Manuel Pastor (University of California Santa Cruz) and Enrique Marcelli (University of Massachusetts Boston) Steven Camarota (Center for Immigration Studies) Frank Morris (University of Texas Dallas) Steven Shulman (Colorado State University) and Hannes Johannsson (Office of the Comptroller of the Currency) and Lisa Catanzarite (University of California Los Angeles). | The Impact of Immigration on African Americans

GBP 130.00
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Statistical Machine Learning A Unified Framework

Statistical Machine Learning A Unified Framework

The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing analyzing evaluating and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students engineers and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular the material in this text directly supports the mathematical analysis and design of old new and not-yet-invented nonlinear high-dimensional machine learning algorithms. Features: Unified empirical risk minimization framework supports rigorous mathematical analyses of widely used supervised unsupervised and reinforcement machine learning algorithms Matrix calculus methods for supporting machine learning analysis and design applications Explicit conditions for ensuring convergence of adaptive batch minibatch MCEM and MCMC learning algorithms that minimize both unimodal and multimodal objective functions Explicit conditions for characterizing asymptotic properties of M-estimators and model selection criteria such as AIC and BIC in the presence of possible model misspecification This advanced text is suitable for graduate students or highly motivated undergraduate students in statistics computer science electrical engineering and applied mathematics. The text is self-contained and only assumes knowledge of lower-division linear algebra and upper-division probability theory. Students professional engineers and multidisciplinary scientists possessing these minimal prerequisites will find this text challenging yet accessible. About the Author: Richard M. Golden (Ph. D. M. S. E. E. B. S. E. E. ) is Professor of Cognitive Science and Participating Faculty Member in Electrical Engineering at the University of Texas at Dallas. Dr. Golden has published articles and given talks at scientific conferences on a wide range of topics in the fields of both statistics and machine learning over the past three decades. His long-term research interests include identifying conditions for the convergence of deterministic and stochastic machine learning algorithms and investigating estimation and inference in the presence of possibly misspecified probability models. | Statistical Machine Learning A Unified Framework

GBP 99.99
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