Machine Learning: An Artificial Intelligence Approach, Volume I

by Ryszard S. Michalski, Jaime G. Carbonell, Tom M. Mitchell

Machine Learning: An Artificial Intelligence Approach contains tutorial overviews and research papers representative of trends in the area of machine learning as viewed from an artificial intelligence perspective. The book is organized into six parts. Part I provides an overview of machine learning and explains why machines should learn. Part II covers important issues affecting the design of learning programs―particularly programs that learn from examples. It also describes inductive learning systems. Part III deals with learning by analogy, by experimentation, and from experience. Parts IV and V discuss learning from observation and discovery, and learning from instruction, respectively. Part VI presents two studies on applied learning systems―one on the recovery of valuable information via inductive inference; the other on inducing models of simple algebraic skills from observed student performance in the context of the Leeds Modeling System (LMS). This book is intended for researchers in artificial intelligence, computer science, and cognitive psychology; students in artificial intelligence and related disciplines; and a diverse range of readers, including computer scientists, robotics experts, knowledge engineers, educators, philosophers, data analysts, psychologists, and electronic engineers.

572 pages, Hardcover

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.