Mathematics
This section describes very briefly some of the mathematical concepts used in the book.
Linear algebra
Many algorithms used in machine learning such as minimization of a convex loss function, principal component analysis, or least squares regression involves invariably manipulation and transformation of matrices. There are many good books on the subject, from the inexpensive [A:2] to the sophisticated [A:3].
QR decomposition
The QR decomposition (also known as QR factorization) is the decomposition of a matrix A into a product of an orthogonal matrix Q and upper triangular matrix R. A=QR and QT Q=I [A:4].
The decomposition is unique if A is a real, square, and invertible matrix. In the case of a rectangle matrix A, m by n with m > n the decomposition is implemented as the dot product of two vector of matrices: A = [Q1 , Q2 ].[R1 , R2 ]T where Q1 is an m by n matrix, Q2 is an m by n matrix, R1 is n by n and an upper triangle matrix, R2 is an m by n null matrix.
QR decomposition...
