Matrix Computations by Gene H. Golub
Golub G.H., Van Loan C.F. Matrix Computations
Johns Hopkins University Press, Springer, Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor.
This course concerns the numerical solution of linear systems, linear least squares problems best approximate solution for an inconsistent linear system , eigenvalue and singular value problems. For the numerical solutions of the problems matrix factorizations will be introduced, and their existence and uniqueness will be discussed. The Krylov-subspace based iterative algorithms will be studied as much as time permits. Office Hours: Tue Wed or by appointment. Course Description: This course concerns the numerical solution of linear systems, linear least squares problems best approximate solution for an inconsistent linear system , eigenvalue and singular value problems.
Golub and Charles F.
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