Machine Learning: An Artificial Intelligence Approach

by Ryszard S. Michalski (Editor), J.G. Carbonell (Editor), T.M. Mitchell (Editor)

The ability to learn is one of the most fundamental attributes of intelligent behavior. Consequently, progress in the theory and computer modeling of learn­ ing processes is of great significance to fields concerned with understanding in­ telligence. Such fields include cognitive science, artificial intelligence, infor­ mation science, pattern recognition, psychology, education, epistemology, philosophy, and related disciplines. The recent observance of the silver anniversary of artificial intelligence has been heralded by a surge of interest in machine learning-both in building models of human learning and in understanding how machines might be endowed with the ability to learn. This renewed interest has spawned many new research projects and resulted in an increase in related scientific activities. In the summer of 1980, the First Machine Learning Workshop was held at Carnegie-Mellon University in Pittsburgh. In the same year, three consecutive issues of the Inter­ national Journal of Policy Analysis and Information Systems were specially devoted to machine learning (No. 2, 3 and 4, 1980). In the spring of 1981, a special issue of the SIGART Newsletter No. 76 reviewed current research projects in the field. . This book contains tutorial overviews and research papers representative of contemporary trends in the area of machine learning as viewed from an artificial intelligence perspective. As the first available text on this subject, it is intended to fulfill several needs.

572 pages, Kindle Edition

First published October 1, 1982

Reviews

[Andrew Miller · Rating 5 out of 5]

It is a comprehensive textbook that covers the fundamentals of machine learning and its applications in various fields, including computer vision, natural language processing, and robotics. The book provides a solid theoretical foundation and also includes practical examples that illustrate how machine learning algorithms can be implemented in real-world scenarios. To further your knowledge and understanding of machine learning you can read https://www.rslonline.com/develop-mac.... The article provides valuable insights into the process of creating accurate and unbiased models by using high-quality labeled data, which is essential for achieving successful predictions.