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This series is in Strata Conference Santa Clara 2014: Complete Video Compilation.
The future belongs to those who understand data.
Gain a clear perspective on the future of big data—and all the analytics, architectures, techniques, tools, and technologies you need to use data successfully. With this complete video compilation, you’ll get a front-row seat to the keynotes, workshops, and sessions at O’Reilly’s Strata Conference Santa Clara 2014.

This textbook thoroughly outlines combinatorial algorithms for generation, enumeration, and search. Topics include backtracking and heuristic search methods applied to various combinatorial structures, such as: · Combinations · Permutations · Graphs · Designs · Many classical areas are covered as well as new research topics not included in most existing texts, such as: · Group algorithms · Graph isomorphism · Hill-climbing · Heuristic search algorithms · This work serves as an exceptional textbook for a modern course in combinatorial algorithms, providing a unified and focused collection of recent topics of interest in the area. The authors, synthesizing material that can only be found scattered through many different sources, introduce the most important combinatorial algorithmic techniques - thus creating an accessible, comprehensive text that students of mathematics, electrical engineering, and computer science can understand without needing a prior course on combinatorics

Introduction to Stochastic Search and Optimization provides comprehensive, current information on methods for real-world problem solving, including stochastic gradient and non-gradient techniques, as well as relatively recent innovations such as simulated annealing, genetic algorithms, and MCMC. It is written to be read and understood by graduate students, industrial practitioners, and experienced researchers in the field. Web links to software and data sets, and an extensive list of references of the book allows the reader to explore deeper into certain topic areas. I also found the index to be very comprehensive and carefully done. The appendices are as a refresher and summary of much of the prerequisite material. The book is somewhat unique in providing a balanced discussion of algorithms, including both their strengths and weaknesses. The book is among very few books that have integrated essential parts of statistical fields with optimization and decision making. The book's inclusion of a chapter on optimal experimental design is an example of such integration. The approaches discussed in the book could be used for financial decision making, forecasting, and quality improvement, among many other areas