Machine Learning Foundations
Supervised, Unsupervised, and Advanced Learning
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Beschreibung
This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning.
- Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning;
- Outlines the computation paradigm for solving classification, regression, and clustering;
- Features essential techniques for building the a new generation of machine learning.
This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning.Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning;Outlines the computation paradigm for solving classification, regression, and clustering;Features essential techniques for building the a new generation of machine learning.
Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning Outlines the computation paradigm for solving classification, regression, and clustering Features essential techniques for building the a new generation of machine learning
Autor*in
Taeho Jo
Themen in »Machine Learning Foundations«
Machine Learning Supervised Learning K nearest Neighbor Naïve Bayes Neural Networks Support Vector Machine Unsupervised Learning K Means Algorithm EM Algorithm Fuzzy Clustering Hierarchical Clustering