MIT Students Making Millions the Easy Way (Horizon 2005)

In the mid-1990s, a team of American science students took on the might of the Las Vegas casinos, and came home with millions of dollars. Hardworking engineering students during the week, they became high-rolling gamblers by the weekend and proved that, in one game at least, the house doesn't always win. The game was blackjack, and the students were from the world-renowned Massachusetts Institute of Technology (MIT). Their audacious winnings marked the climax of an arms race between casino and player that began 40 years earlier with maths professor Edward Thorp. He realised that the one feature of blackjack that made it different from other casino games also made it possible to beat.

Sparse and large-scale learning with heterogeneous data

Google Tech Talks September 5, 2006 Gert Lanckriet is assistant professor in the Electrical and Computer Engineering Department at the University of California, San Diego. He conducts research on machine learning, applied statistics and convex optimization with applications in computational biology, finance, music and vision. ABSTRACT An important challenge for the field of machine learning is to deal with the increasing amount of data that is available for learning and to leverage the (also increasing) diversity of information sources, describing these data. Beyond classical vectorial data formats, data in the format of graphs, trees, strings and beyond have become widely available for data mining, e.g., the linked structure of the world wide web, text, images and sounds on web pages, protein interaction networks, phylogenetic trees, etc. Moreover, for interpretability and economical reasons, decision rules that rely on a small subset of the information sources and/or a small subset of the features describing the data are highly desired: sparse learning algorithms are a must. This talk will outline two recent approaches that address sparse, large-scale learning with heterogeneous data, and show some applications.

Decision Making and Chance

Google Tech Talks September 17, 2006 Dr. Mike Orkin is a Managing Scientist at Exponent, a publicly traded scientific consulting company headquartered in Menlo Park. Mike has numerous research publications in game theory and probability theory and has written data mining and simulation software. He is a nationally known authority on odds and gambling games and has appeared on numerous TV and radio shows to discuss gambling and odds, including CNN, NBC's Dateline and ABC's World News Tonight. ABSTRACT Certain gambling games, such as roulette and craps, are games of pure chance: In repeated play, luck disappears, and the persistent gambler will go broke. Other gambling activities, such as betting on sports or the stock market, may involve an element of skill. One way to measure this is to compare the results of a gambling strategy with chance: A skillful strategy should produce long-run results that are better than would be achieved by someone who is just guessing. One can also compare a gambler’s losses with chance to see if the gambler is doing worse than chance would allow. I will discuss two recent projects that illustrate these concepts: • Automated data mining software discovers that the Baltimore Ravens are 17-3 versus the point spread when they lost their previous game and their opponents played their previous game on the road. Do situations like this give clever gamblers an edge or are such strong win-loss records merely random flukes? • A gambler loses $30 million betting at an online casino. Is it possible to lose this much just by chance or is the gambler being cheated? Or maybe the gambler is part of a money laundering scheme.