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Applied Machine Learning

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Applied Machine Learning Synopsis

Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas.  This book is written for people who want to adopt and use the main tools of machine learning, but aren't necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning, this textbook is a machine learning toolkit. Applied Machine Learning covers many topics for people who want to use machine learning processes to get things done, with a strong emphasis on using existing tools and packages, rather than writing one's own code.


A companion to the author's Probability and Statistics for Computer Science, this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use).

Emphasizing the usefulness ofstandard machinery from applied statistics, this textbook gives an overview of the major applied areas in learning, including coverage of:
classification using standard machinery (naive bayes; nearest neighbor; SVM)
clustering and vector quantization (largely as in PSCS)
PCA (largely as in PSCS)
variants of PCA (NIPALS; latent semantic analysis; canonical correlation analysis)
linear regression (largely as in PSCS)
generalized linear models including logistic regression
model selection with Lasso, elasticnet
robustness and m-estimators
Markov chains and HMM's (largely as in PSCS)
EM in fairly gory detail; long experience teaching this suggests one detailed example is required, which students hate; but once they've been through that, the next one is easy
simple graphical models (in the variational inference section)
classification with neural networks, with a particular emphasis on
image classification
autoencoding with neural networks
structure learning

About This Edition

ISBN: 9783030181161
Publication date: 14th August 2020
Author: David Forsyth
Publisher: Springer an imprint of Springer International Publishing
Format: Paperback
Pagination: 494 pages
Genres: Artificial intelligence
Maths for computer scientists
Probability and statistics