This textbook provides an easy-to-understand introduction to the mathematical concepts and algorithms at the foundation of data science. It covers essential parts of data organization, descriptive and inferential statistics, probability theory, and machine learning. These topics are presented in a clear and mathematical sound way to help readers gain a deep and fundamental understanding. Numerous application examples based on real data are included. The book is well-suited for lecturers and students at technical universities, and offers a good introduction and overview for people who are new to the subject. Basic mathematical knowledge of calculus and linear algebra is required.
The Godel spacetime is an important cosmological solution of Einstein's field equations of gravitation. Although it does not offer a viable description of the physical universe, it illustrates the theoretical possibility of time travel. This work investigates world models similar to the Godel spacetime with particular emphasis on relations between kinematical properties (shear, vorticity, acceleration, expansion) and causality violation, i.e., the formation of closed timelike curves.
Offers insight into the methods and concepts of a very active field of mathematics that has many connections with physics. It includes contributions from specialists in differential geometry and mathematical physics, collectively demonstrating the wide range of applications of Lorentzian geometry, and ranging in character from research papers to surveys to the development of new ideas.
The Godel spacetime is an important cosmological solution of Einstein's field equations of gravitation. Although it does not offer a viable description of the physical universe, it illustrates the theoretical possibility of time travel. This work investigates world models similar to the Godel spacetime with particular emphasis on relations between kinematical properties (shear, vorticity, acceleration, expansion) and causality violation, i.e., the formation of closed timelike curves.
This textbook provides an easy-to-understand introduction to the mathematical concepts and algorithms at the foundation of data science. It covers essential parts of data organization, descriptive and inferential statistics, probability theory, and machine learning. These topics are presented in a clear and mathematical sound way to help readers gain a deep and fundamental understanding. Numerous application examples based on real data are included. The book is well-suited for lecturers and students at technical universities, and offers a good introduction and overview for people who are new to the subject. Basic mathematical knowledge of calculus and linear algebra is required.
Designed to provide English readers of German literature the opportunity to familiarize themselves with both the established canon and newly emerging literatures that reflect the concerns of women and ethnic minorities, the Encyclopedia of German Literature includes more than 500 entries on writers, individual work, and topics essential to an understanding of this rich literary tradition. Drawing on the expertise of an international group of experts, the essays in the encyclopedia reflect developments of the latest scholarship in German literature, culture, and history and society. In addition to the essays, author entries include biographies and works lists; and works entries provide information about first editions, selected critical editions, and English-language translations. All entries conclude with a list of further readings.
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