Applied Machine Learning

• Main Author: Forsyth, David (Author, http://id.loc.gov/vocabulary/relators/aut)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Edition: 1st ed. 2019.
• Subjects: Artificial intelligence.; Mathematical statistics.; Artificial Intelligence.; Probability and Statistics in Computer Science.

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....

Table of 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.