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This print textbook is available for students to rent for their classes. The Pearson print rental program provides students with affordable access to learning materials, so they come to class ready to succeed. For intermediate courses in economics. A unified view of the latest macroeconomic events In Macroeconomics, Blanchard presents an integrated, global view of macroeconomics, enabling students to see the connections between goods markets, financial markets, and labor markets worldwide. Organized into two parts, the text contains a core section that focuses on short-, medium-, and long-run markets and two major extensions that offer more in-depth coverage of the issues at hand. From the major economic crisis that engulfed the world in the late 2000s, to monetary policy in the US, to the problems of the Euro area, and growth in China, the text helps students make sense not only of current macroeconomic events but also of those that may unfold in the future. Integrated, detailed boxes in the 8th Edition have been updated to convey the life of macroeconomics today, reinforce lessons from the models, and help students employ and develop their analytical and evaluative skills. Also available with MyLab Economics By combining trusted author content with digital tools and a flexible platform, MyLab personalizes the learning experience and improves results for each student.
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.