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Empirical Methods for Artificial Intelligence
Paul R. Cohen

Computer science and artificial intelligence in particular have no curriculum in research methods, as other sciences do. This text is the first to present a wide range of empirical methods for AI research.

The first three chapters introduce empirical questions, exploratory data analysis, and experiment design. The blunt interrogation of statistical hypothesis testing is postponed until chapters 4 and 5, which present classical parametric methods and computer-intensive (Monte Carlo) resampling methods, respectively. This is one of few books to present these new, flexible resampling techniques in an accurate, accessible manner.

Much of the book is devoted to research strategies and tactics, introducing new methods in the context of case studies. Chapter 6 covers performance assessment, chapter 7 shows how to identify interactions and dependencies among several factors that explain performance, and chapter 8 discusses predictive models of programs, including causal models. The final chapter asks what counts as a theory in AI, and how empirical methods - which deal with specific systems - can foster general theories.

Mathematical details are confined to appendixes and no prior knowledge of statistics or probability theory is assumed.

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