An introductory text in machine learning that gives a unified treatment of methods based on statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining.
This Third Edition covers newer approaches that have become highly topical, including deep learning, and auto-encoding, introductory information about temporal learning and hidden Markov models, and a much more detailed treatment of ...
The book can be used in both undergraduate and graduate courses; practitioners will find it an essential reference.
Just like electricity, Machine Learning will revolutionize our life in many ways – some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms.
With this book, you’ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data ...
The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner.
The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.
This book addresses the commonalities, and aims to give a thorough and in-depth treatment and develop intuition, while remaining concise.
[46] M. Dredze, R. Gevaryahu, and A. Elias-Bachrach, Learning fast classifiers for image spam, CEAS 2007 Cited on page 298 [47] R. Durbin, S. Eddy, A. Krogh, and G. Mitchison, Biological Sequence Analysis: Probabilistic Models of ...
Technical Report CUED/F-INFENG/TR 166. Engineering Department, Cambridge ... Russo, D. J., Van Roy, B., Kazerouni, A., Osband, I., Wen, Z. (2018). ... Saddoris, M. P., Cacciapaglia, F., Wightmman, R. M., Carelli, R. M. (2015).
The book covers a broad array of topics not usually included in introductory machine learning texts, including supervised learning, Bayesian decision theory, parametric methods, semiparametric methods, nonparametric methods, multivariate ...