Machine Learning: Taxonomy, Knowledge Acquisition, and Learning Mechanisms in Artificial Intelligence
Overview
This paper provides an overview of machine learning as a research area within artificial intelligence, examining its primary goals of building computer models and cognitive skills through observation and experimentation. It discusses the taxonomy of machine learning across three dimensions, explores key learning mechanisms including knowledge acquisition and skill refinement, and analyzes different learning approaches based on the level of inferencing required from learners.
What this coursework covers
- Definition and primary goals of machine learning within artificial intelligence
- Taxonomy of machine learning classified by underlying education, knowledge representation, and application domain
- Learning mechanisms including knowledge acquisition, skill refinement, and analysis of inferencing levels
- Classification of learning approaches: memorization, analogy, learning from examples, and discovery
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Assignment #3 - Summary 1
Machine learning is a research area within artificial intelligence that entails studying and developing computer models. Machine learning’s primary goal is to build computer models and cognitive skills by acquiring new knowledge and discovering new facts through observation and experiments. Task-oriented studies, mental stimulation, and theoretical analysis are the three main focus areas of machine learning. It aims to ease the burden of hand programming in future computers. Machine learning as a science entails the incorporation of other learning mechanisms apart from ordinary human learning. It explores scientific research methods, such as the induction of algorithms, coping with insufficient data, and acquiring information that must be available to the learner.
Knowledge acquisition and skill refinement are the primary methods of machine learning. Knowledge acquisition entails developing or learning new data such as physical laws and incorporating the knowledge effectively. Skill refinement entails the improvement of both motor and cognitive skills through regular practice. The taxonomy of machine learning is essential in comparing and classifying artificial intelligence-based machine learning. The taxonomy of machine learning classifies it into three dimensions based on underlying education, based on a representation of knowledge or skill, and in terms of an application domain.
In distinguishing learning approaches, it is essential to focus on the level of inferencing done by learners. It is challenging to program a computer to undertake a complex task compared to delegating the task to humans through instructions.