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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