A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems.
In many practical situations it is impossible to run existing machine learning methods on a single computer, because either the data is too... Læs mere
This book gives a solid basis for conducting performance evaluations of learning algorithms in practical settings with an emphasis on... Læs mere
Understanding when and why algorithms work is a fundamental challenge. For problems ranging from clustering to linear programming to neural networks there are... Læs mere
P-splines are widely used in statistics and machine learning for smoothing out noise in data and to avoid overtraining. This practical guide covers theory and a... Læs mere
This accessible but rigorous introduction is written for advanced undergraduates and beginning graduate students in data... Læs mere
Machine Learning brings together all the state-of-the-art methods for making sense of data. With hundreds of worked examples and... Læs mere
Understand fundamental and advanced statistical and deep learning models for robust speaker recognition and domain adaptation. Presenting state-of-the-art machine... Læs mere
The first unified treatment of the interface between information theory and emerging topics in data science, written in a clear, tutorial style. Covering... Læs mere
Grammatical inference connects with many scientific disciplines, including bio-informatics, computational linguistics and pattern recognition. This... Læs mere
This book provides some important findings about the general patterns of use of Bengali morphemes and sheds new light on the form and function of morphemes in... Læs mere