The Perfect Bet: How Science and Maths are Taking the Luck Out of Gambling

"An elegant and amusing account.... Anyone planning to enter a casino or place an online bet would be advised to keep this book handy." — Wall Street Journal For the past 500 years, gamblers-led by mathematicians and scientists-have been trying to figure out how to pull the rug out from under Lady Luck. In The Perfect Bet , mathematician and award-winning writer Adam Kucharski tells the astonishing story of how the experts have succeeded, revolutionizing mathematics and science in the process. The house can seem unbeatable. Kucharski shows us just why it isn't. Even better, he demonstrates how the search for the perfect bet has been crucial for the scientific pursuit of a better world. "An elegant and amusing account" of how gambling has been reshaped by the application of science and revealed the truth behind a lucky bet ( Wall Street Journal) . For the past 500 years, gamblers-led by mathematicians and scientists-have been trying to figure out how to pull the rug out from under Lady Luck. In The Perfect Bet , mathematician and award-winning writer Adam Kucharski tells the astonishing story of how the experts have succeeded, revolutionizing mathematics and science in the process. The house can seem unbeatable. Kucharski shows us just why it isn't. Even better, he demonstrates how the search for the perfect bet has been crucial for the scientific pursuit of a better world. This book constitutes the refereed proceedings of the 12th European Conference on Machine Learning, ECML 2001, held in Freiburg, Germany, in September 2001. The 50 revised full papers presented together with four invited contributions were carefully reviewed and selected from a total of 140 submissions. Among the topics covered are classifier systems, naive-Bayes classification, rule learning, decision tree-based classification, Web mining, equation discovery, inductive logic programming, text categorization, agent learning, backpropagation, reinforcement learning, sequence prediction, sequential decisions, classification learning, sampling, and semi-supervised learning. * A multi-volume major reference work, presenting a timely and comprehensive body of knowledge designed to serve as an essential reference for the finance community. * Includes contributions from leading figures in industry and academic research and provides an authoritative exposition of quantitative finance. From roulette tables to racetracks, provides a look at how scientists and mathematicians throughout the years have tried to figure out how to beat the house while gambling