 # From Algorithms to Z-Scores: Probabilistic and Statistical Modeling in Computer Science

## Professor Norm Matloff , University of California, Davis

OVERVIEW:

The materials here form a textbook for a course in mathematical probability and statistics for computer science students. (It would work fine for general students too.)

"Why is this text different from all other texts?"

• 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 sophistication in programming can be assumed. It is recommended that my R tutorials 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?

For instance, the chapter on continuous random variables begins by explaining that such distributions do not actually exist in the real world, due to the discreteness of our measuring instruments. The continuous model is therefore just that--a model, and indeed a very useful model.

There is actually an entire chapter on modeling, discussing the tradeoff between accuracy and simplicity of models.

• There is considerable discussion of the intuition involving probabilistic concepts, and the concepts themselves are defined through intuition. 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.

The materials are continuously evolving, with new examples and topics being added.

Prerequisites: The student must know calculus, basic matrix algebra, and have some minimal skill in programming.

LICENSING: This work is licensed under a Creative Commons Attribution-No Derivative Works 3.0 United States License. Copyright is retained by N. Matloff in all non-U.S. jurisdictions, but permission to use these materials in teaching is still granted, provided the authorship and licensing information here is displayed. I would appreciate being notified if you use this book for teaching, just so that I know the materials are being put to use, but this is not required.