Advanced Lectures on Machine Learning
Springer Berlin (Verlag)
978-3-540-23122-6 (ISBN)
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.
| Erscheint lt. Verlag | 2.9.2004 |
|---|---|
| Reihe/Serie | Lecture Notes in Artificial Intelligence | Lecture Notes in Computer Science |
| Zusatzinfo | X, 246 p. |
| Verlagsort | Berlin |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 405 g |
| Themenwelt | Informatik ► Software Entwicklung ► User Interfaces (HCI) |
| Informatik ► Theorie / Studium ► Algorithmen | |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | Algorithm analysis and problem complexity • Algorithmic Learning • Bayesian inference • classification • classifier systmes • Inductive Inference • learning • Learning Algorithms • Learning theory • machine learning • Maschinelles Lernen • Statistical Learning • stochastic learning • Unsupervised Learning |
| ISBN-10 | 3-540-23122-6 / 3540231226 |
| ISBN-13 | 978-3-540-23122-6 / 9783540231226 |
| Zustand | Neuware |
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