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  Network Science
Posted on 2016-05-23 (Send to a Friend Send to a Friend )
Network Science Networks are everywhere, from the Internet, to social networks, and the genetic networks that determine our biological existence. Illustrated throughout in full colour, this pioneering textbook, spanning a wide range of topics from physics to computer science, engineering, economics and the social sciences, introduces network science to an interdisciplinary audience. From the origins of the six degrees of separation to explaining why networks are robust to random failures, the author explores how viruses like Ebola and H1N1 spread, and why it i . . . .
 
 
  Pattern Recognition and Machine Learning
Posted on 2016-05-12 (Send to a Friend Send to a Friend )
This new textbook reflects these recent developments while providing a compre- hensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or ma- chine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not es- sential as the book includes a self-contai . . . .
 
 
  Statistical Learning and Sequential Prediction
Posted on 2016-05-11 (Send to a Friend Send to a Friend )
1 About This course will focus on theoretical aspects of Statistical Learning and Sequential Prediction . Until recently, these two subjects have been treated separately within the learning community. The course will follow a unified approach to analyzing learning in both scenarios. To make this happen, we shall bring together ideas from probability and statistics, game theory, algorithms, and optimization. It is this blend of ideas that makes the subject interesting for us, and we hope to convey the excitement. We shall tr . . . .
 
 
  An Introduction to Statistical Learning: with Applications in R
Posted on 2016-05-09 (Send to a Friend Send to a Friend )
An Introduction  to Statistical  Learning: with Applications in R One of the first books in this area— The Elements of Statistical Learning (ESL) (Hastie, Tibshirani, and Friedman)—was published in 2001, with a second edition in 2009. ESL has become a popular text not only in statis- tics but also in related fields. One of the reasons for ESL’s popularity is its relatively accessible style. But ESL is intended for individuals with ad- vanced training in the mathematical sciences. An Introduction to Statistical Learning (ISL) arose from the perceived need for a broader and less tech- nical treatment . . . .
 
 
  The Elements of Statistical Learning
Posted on 2016-05-08 (Send to a Friend Send to a Friend )
The Elements of Statistical Learning This book is our attempt to bring together many of the important new ideas in learning, and explain them in a statistical framework. While some mathematical details are needed, we emphasize the methods and their con- ceptual underpinnings rather than their theoretical properties. As a result, we hope that this book will appeal not just to statisticians but also to researchers and practitioners in a wide variety of fields. . . . .
 
 
   Dynamical screening in correlated electron systems - from lattice models to realistic materials
Posted on 2016-03-25 (Send to a Friend Send to a Friend )
Recent progress in treating the dynamically screened nature of the Coulomb interaction in strongly correlated lattice models and materials is reviewed with a focus on computational schemes based on the dynamical mean field approximation. We discuss approximate and exact methods for the solution of impurity models with retarded interactions, and explain how these models appear as auxiliary problems in various extensions of the dynamical mean field formalism. The current state of the field is illustrated with results from recent applications of t . . . .
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