Metal Men: How Marc Rich Defrauded the Country, Evaded the Law, and Became the World's Most Sought-After Corporate Criminal

Heavy Metal
Marc Rich -- the most wanted white-collar criminal in America -- was one of the most successful metal traders in the world. Before there was Michael Milken or Ivan Boesky, Rich rose through the ranks to amass a multibillion dollar fortune in the halcyon days of high-flying commodities trading. But he did it by cutting corners and pulling the wool over the eyes of his competitors. Eventually his companies pleaded guilty to 38 counts of tax evasion, paying $90 million in fines. Rich fled to Switzerland, where he faced a potential jail term of over 300 years if he ever returned to the United States. This is a story of greed, corruption, and money gone wild, in truly astronomical proportions. Posing as a commodities trader, A. Craig Copetas goes behind the scenes to give us a riveting, true-to-life portrait of Rich's corrupt world and his incredible escape from the law.

Foundations of Machine Learning (Adaptive Computation and Machine Learning series)

Machine Learning
This graduate-level textbook introduces fundamental concepts and methods in machine learning. It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning fills the need for a general textbook that also offers theoretical details and an emphasis on proofs. Certain topics that are often treated with insufficient attention are discussed in more detail here; for example, entire chapters are devoted to regression, multi-class classification, and ranking. The first three chapters lay the theoretical foundation for what follows, but each remaining chapter is mostly self-contained. The appendix offers a concise probability review, a short introduction to convex optimization, tools for concentration bounds, and several basic properties of matrices and norms used in the book.The book is intended for graduate students and researchers in machine learning, statistics, and related areas; it can be used either as a textbook or as a reference text for a research seminar.

Used Book in Good Condition