Applied Machine Learning / by David Forsyth.

Format:

Book

Author/Creator:

Forsyth, David, author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Language:

English

Subjects (All):

Artificial intelligence.

Mathematical statistics.

Artificial Intelligence.

Probability and Statistics in Computer Science.

Local Subjects:

Artificial Intelligence.

Probability and Statistics in Computer Science.

Physical Description:

1 online resource (XXI, 494 pages) : 159 illustrations, 86 illustrations in color

Edition:

First edition 2019.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2019.

System Details:

text file PDF

Summary:

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 of standard machinery from applied statistics, this textbook gives an overview of the major applied areas in learning Covers the ideas in machine learning that everyone going to use learning tools should know, whatever their chosen specialty or career. Broad coverage of the area ensures enough to get the reader started, and to realize that it's worth knowing more in-depth knowledge of the topic. Practical approach emphasizes using existing tools and packages quickly, with enough pragmatic material on deep networks to get the learner started without needing to study other material.

Contents:

1. Learning to Classify

2. SVM's and Random Forests

3. A Little Learning Theory

4. High-dimensional Data

5. Principal Component Analysis

6. Low Rank Approximations

7. Canonical Correlation Analysis

8. Clustering

9. Clustering using Probability Models

10. Regression

11. Regression: Choosing and Managing Models

12. Boosting

13. Hidden Markov Models

14. Learning Sequence Models Discriminatively

15. Mean Field Inference

16. Simple Neural Networks

17. Simple Image Classifiers

18. Classifying Images and Detecting Objects

19. Small Codes for Big Signals

Index.

Other Format:

Printed edition:

ISBN:

978-3-030-18114-7

9783030181147

9783030181130

9783030181154

9783030181161

Access Restriction:

Restricted for use by site license.