Empirical Methods for Artificial Intelligence
||Author: Paul R. Cohen|
List Price: $75.00
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Publisher: MIT Press (03 August, 1995)
Sales Rank: 354,629
Average Customer Rating: 5 out of 5
Customer ReviewsRating: 5 out of 5
Excellent introduction to experimental science
The title of the book could have been easily "Empirical Methods for Computer Science" or even "Empirical Methods for Science."
The goal of the book is to give a gentle but solid introduction into empirical research, experimental science and interpretation of data.
First four chapters are really a must-read for anyone who is interested in empirical methods. In the first chapter "Empirical Research", the author lays the foundations. Chapter two "Exploratory Data Analysis" starts with the fundamentals of statistics of one variable and introduces time series and execution traces. I really loved the "Fitting functions to Data in Scatterplots" subchapter. The introduction continues in the third chapter "Basic Issues in Experimental Design" where we learn about control, spurious effects, sampling bias, dependent variables and pilot experiments. The author gives some nice advices here. Fourth chapter is "Hypothesis Testing and Estimation" and this one concludes the introductory part.
Chapters 5-9 are a little bit more advanced and somewhat biased towards Computer Science and Artificial Intelligence but could be an interesting and refreshing read to anyone who wants to get a solid foundation to experiment design, execution, data collection and interpretation.
The author uses experimental data generated by a system called "Phoenix" (which he codeveloped) as the main example in the book.