Values and Patterns in Data Learning - 2009

Explore the intersection of discrete values and appearance-order patterns in data analysis through this comprehensive lecture by Alon Orlitsky from the University of California, San Diego. Delve into four key applications: distribution modeling, probability estimation, data compression, and classification. Discover why discrete values should be considered primarily for their appearance-order patterns when learning from data. Encounter the works and ideas of influential figures such as Laplace, Good, Turing, Hardy, Ramanujan, Fisher, Shakespeare, and Shannon throughout the presentation. Gain insights from Orlitsky's extensive background in electrical engineering, computer science, and information theory, including his experiences at Bell Laboratories, D.E. Shaw and Company, and the University of California, San Diego. Learn from an IEEE fellow and recipient of multiple prestigious awards as he shares his expertise in information theory, statistical modeling, machine learning, and speech recognition.