Mathematics
This section describes very briefly some of the mathematical concepts used in this book.
Linear algebra
Many algorithms used in machine learning such as minimization of a convex loss function, principal component analysis, or least squares regression invariably involve 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
QR decomposition (or QR factorization) is the decomposition of a matrix A into a product of an orthogonal matrix Q and upper triangular matrix R. So, A=QR and QTQ=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 vectors of matrix A = [Q1, Q2].[R1, R2]T, where Q1 is an m by n matrix, Q2 is an m by n matrix, R1 is an n by n upper triangular matrix, and R2 is an m by n null matrix.
QR decomposition is a reliable...
