Parallel And Distributed Computing

This course provides graduate students in computer science and in other fields of science and engineering with experience of parallel and distributed computing. It gives an overview of parallel and distributed computers, and parallel computation. The course addresses architectures, languages, environments, communications, and parallel programming. Emphasis on understanding parallel and distributed computers and portable parallel programming with MPI.

Matx Thry/Num Lin Alg II

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.

Data Mining

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.

Tops Artific Intell(Sub)

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.

Subscribe to