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Machine learning Lecture 2
The document summarizes key concepts in machine learning including concept learning as search, general-to-specific learning, version spaces, candidate elimination algorithm, and decision trees. It discusses how concept learning can be viewed as searching a hypothesis space to find the hypothesis that best fits the training examples. The candidate elimination algorithm represents the version space using the most general and specific hypotheses to efficiently learn from examples.
