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Dynamic Programming is a fundamental tool widely used to model and solve various sequential decision making under uncertainty. This course is developed to study the popular concepts and techniques of dynamic programming. The contents include Principles of Optimality; Deterministic Dynamic Programming Problems; Stochastic Dynamic Programming Problems with Perfect and Imperfect Information; Infinite Horizon Problem and Dynamic Programming Algorithm. Some applications of Approximate Dynamic Programming, especially for problems from operations research and computer science will be discussed.
An explosion of Web-based language techniques, merging of distinct fields, availability of phone-based dialogue systems, and much more make this an exciting time in speech and language processing. The first of its kind to thoroughly cover language technology – at all levels and with all modern technologies – this text takes an empirical approach to the subject, based on applying statistical and other machine-learning algorithms to large corporations. The authors cover areas that traditionally are taught in different courses, to describe a unified vision of speech and language processing. Emphasis is on practical applications and scientific evaluation. An accompanying Website contains teaching materials for instructors, with pointers to language processing resources on the Web. The Second Edition offers a significant amount of new and extended material.
Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools.
