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Part One: General Issues in Machin Learning
Ch1: An overview of machine learning
Ch2: why should machine learn
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Part Two: Learning from Examples
Ch3: A comparative review of selected methods for learning from examples
Ch4: A theory and methodology of inductive learning
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Part Three: Learning in Problem-Solving and Planning
Ch5: Learning by analogy: formulating and generalizing plans from past experience
Ch6: Learning by experimentatiion: acquiring and refining problem-solving heuristics
Ch7: Acquisition of proof skills in geomety
Ch8: Using proofs and refutations to learn from experience
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Part Four: Learning from Observation and Discovery
Ch9: The role of heuristics in learning by discovery: 3 case studies
Ch10: Rediscovering chemistry with the BACON system
Ch11: Learning from observation: conceptional clustering
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Part Five: Learning from Instruction
Ch12: Machine Transformation of advice into a heuristic search procedure
Ch13: Learning by being told: acquiring knowledge for information management
Ch14: The instructible production systems: a retrospective analysis
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Part Six: Applied Learning Systems
Ch15: Learning efficient classification procedures and their application to chess end games
Ch16: Inferring student models for intelligent computer-aided instruction
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