Computer Science/Discrete Mathematics Seminar I

Via four applications: distribution modeling, probability estimation, data compression, and classification, we argue that when learning from data, discrete values should be ignored except for just their appearance-order pattern. Along the way, we encounter Laplace, Good, Turing, Hardy, Ramanujan, Fisher, Shakespeare, and Shannon. The material will be presented in two independent parts. Based on work with P. Santhanam, K. Viswanathan, J. Zhang, and others.