Numerical solution of matrix eigenvalue problems and applications of eigenvalues. Normal forms of Jordan and Schur. Vector and matrix norms. Perturbation theory and bounds for eigenvalues. Stable matrices and Lyapunov theorems. Nonnegative matrices. Iterative methods for solving large sparse linear systems.

The course will introduce the fundamental principles and main techniques in the area of data mining and its applications. The topics covered include association rule mining, clustering, classification, feature selection, similarity search, data cleaning, privacy and security issues, as well as a wide spectrum of data mining applications in the area of biomedical informatics, bioinformatics, financial market study, image processing, network monitoring and social service analysis.

Advanced topics chosen from the following: knowledge representation, knowledge acquisition, problem solving, very high-level programming languages, expert systems, intelligent and deductive databases, automated theorem proving. May be repeated to a maximum of six credits, but only three credits may be earned under the same topic.

Topics to be selected by staff. May be repeated to a maximum of six credits but only three credits may be earned under the same topic.

Topics to be selected by staff. May be repeated to a maximum of six credits but only three credits may be earned under the same topic.

Half-time to full-time work on thesis. May be repeated to a maximum of six semesters.

Half-time to full-time work on thesis. May be repeated to a maximum of six semesters.

Half-time to full-time work on thesis. May be repeated to a maximum of six semesters.

Half-time to full-time work on thesis. May be repeated to a maximum of six semesters.

Half-time to full-time work on thesis. May be repeated to a maximum of six semesters.