| dc.description.abstract | Taken literally, the title "All of Statistics" is an exaggeration. But in spirit,
the title is apt, as the book does cover a much broader range of topics than a
typical introductory book on mathematical statistics.
This book is for people who want to learn probability and statistics quickly.
It is suitable for graduate or advanced undergraduate students in computer
science, mathematics, statistics, and related disciplines. The book includes
modern topics like nonparametric curve estimation, bootstrapping, and classification,
topics that are usually relegated to follow-up courses. The reader is
presumed to know calculus and a little linear algebra. No previous knowledge
of probability and statistics is required.
Statistics, data mining, and machine learning are all concerned with
collecting and analyzing data. For some time, statistics research was conducted
in statistics departments while data mining and machine learning research
was conducted in computer science departments. Statisticians thought
that computer scientists were reinventing the wheel. Computer scientists
thought that statistical theory didn't apply to their problems.
Things are changing. Statisticians now recognize that computer scientists
are making novel contributions while computer scientists now recognize the
generality of statistical theory and methodology. Clever data mining algorithms
are more scalable than statisticians ever thought possible. Formal statistical
theory is more pervasive than computer scientists had realized.
Students who analyze data, or who aspire to develop new methods for
analyzing data, should be well grounded in basic probability and mathematical
statistics. Using fancy tools like neural nets, boosting, and support vector machines without understanding basic statistics is like doing brain surgery
before knowing how to use a band-aid. | en_US |
