Advanced Lectures on Machine Learning ML Summer Schools 2003, Canberra, Australia, February 2 - 14, 2003, Tübingen, Germany, August 4 - 16, 2003, Revised Lectures /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Bousquet, Olivier (Επιμελητής έκδοσης), Luxburg, Ulrike von (Επιμελητής έκδοσης), Rätsch, Gunnar (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2004.
• Σειρά: Lecture Notes in Computer Science, 3176
• Θέματα: Computer science.; Computers.; Algorithms.; Artificial intelligence.; Pattern recognition.; Computer Science.; Artificial Intelligence (incl. Robotics).; Computer Science, general.; Algorithm Analysis and Problem Complexity.; Computation by Abstract Devices.; Pattern Recognition.

 Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600. This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in Tübingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references. Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning. 

 An Introduction to Pattern Classification -- Some Notes on Applied Mathematics for Machine Learning -- Bayesian Inference: An Introduction to Principles and Practice in Machine Learning -- Gaussian Processes in Machine Learning -- Unsupervised Learning -- Monte Carlo Methods for Absolute Beginners -- Stochastic Learning -- to Statistical Learning Theory -- Concentration Inequalities.