“Worry never robs tomorrow of its sorrow, but only saps today of its strength.” - A.J. Cronin

“Elements of Statistical Learning” (ESL) is often referred to as the “bible” for any person interested in building statistical models. The content in ESL is dense and the implicit prerequisites are good background in linear algebra, calculus and some exposure to statistical inference and prediction. Also, the visuals in ESL come with no elaborate code. If you don’t want to take them at face value and would like to check the statements / visuals in the book, you have got to sweat it out.

“Dummies” books, despite their popularity are scorned by experienced programmers for various reasons. One of them, I guess is that such books lead a newbie in to understanding the subject as a motley collection of recipes for various tasks. Be that as it may, this book is a very well organized book catering to a newbie R programmer. A few years ago, books such as these on R were just not available.

Matrices are everywhere in statistics. Every linear model involves some kind of a generalized inverse calculation of the design matrix. Statisticians, data analysts etc. are always dealing with datasets that might have repeated measurements, repeated observations, noise etc. The matrix is never full row rank matrix. This means that the traditional inverse applicable for a square matrix has to give way to something less restrictive and hence a generalized inverse is needed.

One can conveniently describe a linear programming problem by stating the goal as minimizing / maximizing a function given a set of constraints. The word “linear” restricts the objective function and constraints to have a linear form. Despite this seemingly harsh restriction, LP can be used to attack a LOT of problems. This book is a Linear Programming 101 book and a book with visuals. I am a big fan of books with visuals.