June 2019
Intermediate to advanced
645 pages
18h 18m
English
Markus Levy NXP Semiconductors, Eindhoven, The Netherlands
In this chapter we start by introducing machine learning (ML). We explain the terminology such as supervised and unsupervised ML. We explain the ML tasks called classification and regression. Then we introduce algorithms such as nearest neighbor or support vector machine (SVM), and speak about decision trees and in reference to an example of decision trees we explain ensemble techniques as well as boosting and bagging techniques.
Artificial intelligence; Machine learning; Semantic gap; Data augmentation; Neural network; Classification; Regression; CNN; RNN