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The Political Machine - Assembling Sovereignty in the Bronze Age Caucasus - Adam T. Smith

Migrants and Machine Politics - Tariq Thachil - Bog - Princeton University Press - Plusbog.dk

Migrants and Machine Politics - Tariq Thachil - Bog - Princeton University Press - Plusbog.dk

How poor migrants shape city politics during urbanization As the Global South rapidly urbanizes, millions of people have migrated from the countryside to urban slums, which now house one billion people worldwide. The transformative potential of urbanization hinges on whether and how poor migrants are integrated into city politics. Popular and scholarly accounts paint migrant slums as exhausted by dispossession, subdued by local dons, bought off by wily politicians, or polarized by ethnic appeals. Migrants and Machine Politics shows how slum residents in India routinely defy such portrayals, actively constructing and wielding political machine networks to demand important, albeit imperfect, representation and responsiveness within the country’s expanding cities.Drawing on years of pioneering fieldwork in India’s slums, including ethnographic observation, interviews, surveys, and experiments, Adam Michael Auerbach and Tariq Thachil reveal how migrants harness forces of political competition—as residents, voters, community leaders, and party workers—to sow unexpected seeds of accountability within city politics. This multifaceted agency provokes new questions about how political networks form during urbanization. In answering these questions, this book overturns longstanding assumptions about how political machines exploit the urban poor to stifle competition, foster ethnic favoritism, and entrench vote buying.By documenting how poor migrants actively shape urban politics in counterintuitive ways, Migrants and Machine Politics sheds new light on the political consequences of urbanization across India and the Global South.

DKK 854.00
3

Machine Learning for Physics and Astronomy - Viviana Acquaviva - Bog - Princeton University Press - Plusbog.dk

Machine Learning for Physics and Astronomy - Viviana Acquaviva - Bog - Princeton University Press - Plusbog.dk

A hands-on introduction to machine learning and its applications to the physical sciences As the size and complexity of data continue to grow exponentially across the physical sciences, machine learning is helping scientists to sift through and analyze this information while driving breathtaking advances in quantum physics, astronomy, cosmology, and beyond. This incisive textbook covers the basics of building, diagnosing, optimizing, and deploying machine learning methods to solve research problems in physics and astronomy, with an emphasis on critical thinking and the scientific method. Using a hands-on approach to learning, Machine Learning for Physics and Astronomy draws on real-world, publicly available data as well as examples taken directly from the frontiers of research, from identifying galaxy morphology from images to identifying the signature of standard model particles in simulations at the Large Hadron Collider. - Introduces readers to best practices in data-driven problem-solving, from preliminary data exploration and cleaning to selecting the best method for a given task - Each chapter is accompanied by Jupyter Notebook worksheets in Python that enable students to explore key concepts - Includes a wealth of review questions and quizzes - Ideal for advanced undergraduate and early graduate students in STEM disciplines such as physics, computer science, engineering, and applied mathematics - Accessible to self-learners with a basic knowledge of linear algebra and calculus - Slides and assessment questions (available only to instructors)

DKK 357.00
3

Theme for Reason - James Ward Smith - Bog - Princeton University Press - Plusbog.dk

Machine Learning in Asset Pricing - Stefan Nagel - Bog - Princeton University Press - Plusbog.dk

Machine Learning in Asset Pricing - Stefan Nagel - Bog - Princeton University Press - Plusbog.dk

A groundbreaking, authoritative introduction to how machine learning can be applied to asset pricing Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly becoming part of the toolkit in asset pricing research and quantitative investing. In this book, Stefan Nagel examines the promises and challenges of ML applications in asset pricing.Asset pricing problems are substantially different from the settings for which ML tools were developed originally. To realize the potential of ML methods, they must be adapted for the specific conditions in asset pricing applications. Economic considerations, such as portfolio optimization, absence of near arbitrage, and investor learning can guide the selection and modification of ML tools. Beginning with a brief survey of basic supervised ML methods, Nagel then discusses the application of these techniques in empirical research in asset pricing and shows how they promise to advance the theoretical modeling of financial markets. Machine Learning in Asset Pricing presents the exciting possibilities of using cutting-edge methods in research on financial asset valuation.

DKK 413.00
3