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.