Introduction to Machine Learning

Constraints are everywhere: most computational problems can be described in terms of restrictions imposed on the set of possible solutions, and constraint programming is a problem-solving technique that works by incorporating those restrictions in a programming environment. It draws on methods from combinatorial optimisation and artificial intelligence, and has been successfully applied in a number of fields from scheduling, computational biology, finance, electrical engineering and operations research through to numerical analysis. This textbook for upper-division students provides a thorough and structured account of the main aspects of constraint programming. The author provides many worked examples that illustrate the usefulness and versatility of this approach to programming, as well as many exercises throughout the book that illustrate techniques, test skills and extend the text. Pointers to current research, extensive historical and bibliographic notes, and a comprehensive list of references will also be valuable to professionals in computer science and artificial intelligence. Constraints are everywhere: most computational problems can be described in terms of restrictions imposed on the set of possible solutions, and constraint programming is a problem-solving technique that works by incorporating those restrictions in a programming environment. It draws on methods from combinatorial optimization and artificial intelligence, and has been successfully applied in a number of fields from scheduling, computational biology, finance, electrical engineering and operations research through to numerical analysis. This textbook for upper-division students provides a thorough and structured account of the main aspects of constraint programming. The author provides many worked examples that illustrate the usefulness and versatility of this approach to programming, as well as many exercises throughout the book that illustrate techniques, test skills and extend the text. Pointers to current research, extensive historical and bibliographic notes, and a comprehensive list of references will also be valuable to professionals in computer science and artificial intelligence. Scheduling, vehicle routing and timetabling are all examples of constraint problems, and methods to solve them rely on the idea of constraint propagation and search. This book meets the need for a modern, multidisciplinary introduction to the field that covers foundations and applications. Written by Krzysztof Apt, an authority on the subject, it will be welcomed by graduate students and professionals. With the insertion of constraint techniques into programming environments, new developments have accelerated the solution process. Constraint programming combines ideas from artificial intelligence, programming languages, databases, and operational research. Preface......Page 5 Introduction......Page 11 Density Estimation......Page 45 Optimization......Page 99 Online Learning and Boosting......Page 151 Conditional Densities......Page 157 Kernels and Function Spaces......Page 163 Linear Models......Page 173 to 1.15Appendix 1Linear Algebra and Functional Analysis......Page 205 to 1.15Appendix 2Conjugate Distributions......Page 209 to 1.15Appendix 3Loss Functions......Page 211 Bibliography......Page 227 Methods to solve constraint problems (e.g. scheduling) rely on the idea of constraint propagation and search. Using constraint techniques in programming environments has accelerated the solution process, combining ideas from artificial intelligence, programming languages, databases, and operational research. This text is a modern, multidisciplinary introduction covering both foundations and applications

upper-division Textbook Covering Foundations Of Constraint Programming And Applications To Scheduling, Optimisation Etc.