From Algorithms to Z-Scores: Probabilistic and Statistical Modeling in Computer Science
The materials here form a textbook for a course in mathematical probability and statistics for computer science students. "Why is this course different from all other courses?" * Computer science examples are used throughout, in areas such as: computer networks; data and text mining; computer security; remote sensing; computer performance evaluation; software engineering; data management; etc. * The R statistical/data manipulation language is used throughout. Since this is a computer science audience, a greater sophitication in programming can be assumed. It is recommended that my R tutorial, R for Programmers, be used as a supplement. * Throughout the units, mathematical theory and applications are interwoven, with a strong emphasis on modeling: What do probabilistic models really mean, in real-life terms? How does one choose a model? How do we assess the practical usefulness of models? * There is considerable discussion of the intuition involving probabilistic concepts. However, all models and so on are described precisely in terms of random variables and distributions. For topical coverage, see the book's detailed table of contents.
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