Machine Learning Foundations: Supervised, Unsupervised, and Advanced Learning

ISBN-13: 9783030658991 / Angielski / Twarda / 2021 / 391 str.

Taeho Jo

Spis treści

Part I. Foundation.- Chapter 1. Introduction.- Chapter 2. Numerical Vectors.- Chapter 3.Data Encoding.- Chapter 4. Simple Machine Learning Algorithms.- Part II. Supervised Learning.- Chapter 5. Instance based Learning.- Chapter 6. Probabilistic Learning.- Chapter 7. Decision Tree.- Chapter 8. Support Vector Machine.- Part III. Unsupervised Learning.- Chapter 9. Simple Clustering Algorithms.- Chapter 10. K Means Algorithm.- Chapter 11. EM Algorithm.- Chapter 12. Advanced Clustering.- Part IV. Advanced Topics.- Chapter 13. Ensemble Learning.- Chapter 14. Semi-Supervised Learning.- Chapter 15. Temporal Learning.- Chapter 16. Reinforcement Learning.

O autorze

Taeho Jo is the president and the founder of the company, Alpha Lab AI which makes business concerned with Artificial Intelligence. He received his Bachelor, Master, and PhD degrees from Korea University in 1994, from Pohang University in 1997, and from University of Ottawa, 2006, respectively. He has published more than 180 research papers, primarily in text mining, machine learning, neural networks, and information retrieval. He previously published the book “Text Mining: Concept, Implementation, and Big Data Challenge” (Springer 2018).

Blurb

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.