Algorithms for Decision Making

This book provides a broad introduction to algorithms for decision  making under uncertainty. We cover a wide variety of topics related to  decision making, introducing the underlying mathematical problem  formulations and the algorithms for solving them. Figures, examples, and  exercises are provided to convey the intuition behind the various  approaches.
This text is intended for advanced undergraduates and  graduate students as well as professionals. The book requires some  mathematical maturity and assumes prior exposure to multivariable  calculus, linear algebra, and probability concepts. Some review material  is provided in the appendix. Disciplines where the book would be  especially useful include mathematics, statistics, computer science,  aerospace, electrical engineering, and operations research. Fundamental  to this textbook are the algorithms, which are all implemented in the  Julia programming language. We have found the language to be ideal for  specifying algorithms in human readable form. The priority in the design  of the algorithmic implementations was interpretability rather than  efficiency. Industrial applications, for example, may benefit from  alternative implementations. Permission is granted, free of charge, to  use the code snippets associated with this book, subject to the  condition that the source of the code is acknowledged.