“An Overview of Machine Learning”: Partial Summary

Running head: AN OVERVIEW OF MACHINE LEARNING1
“An Overview of Machine Learning”: Partial Summary
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AN OVERVIEW OF MACHINE LEARNING

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“An Overview of Machine Learning”: Partial Summary
In “An Overview of Machine Learning,” Carbonell, Michalski and Mitchell (1983) offer an introduction into importance of machine learning/artificial intelligence (AI), areas of primary research interests, or foci, and major categories, or taxonomies, in machine learning, or AI, research. The authors start by identifying Task-Oriented Studies, Cognitive Simulation and Theoretical Analysis (pp. 3-4) as primary foci of machine learning. The machine learning discipline, explain authors, is critically necessary in order for humans to develop new bodies of knowledge and new skills in more systematic ways. In contrast to, say, planes, machine learning, argue authors, must simulate humans since humans are, after all, end users of machines. The authors also draw an important distinction between “knowledge acquisition” and “skill refinement.” In essence, knowledge acquisition is about developing a new body of knowledge using different strategies (discussed soon), whereas skill refinement is about applying conceptual frameworks and acquired knowledge to certain practical situations such as, say, learning to drive a bike.
The overview on why machine learning is important ...