290 Courses for Spring 2008
For help in enrolling in these courses, please contact the Undergraduate Advisor.
290B. Java-centric network computing services
Instructor: Cappello
Java-centric network computing services: Java Remote Method Invocation; design patterns and work-stealing; system, language, and algorithmic/complexity issues related to network computing.
Class satisfies these areas for MS students only: Systems
Enrollment Code: 08136
Day and Time: TR 100-300
Location: Phelps 1401
Units: 4.0, letter grade ONLY
290F. Intelligent Wireless Systems
Instructor: Zheng
Intelligent Wireless Systems: a special topics graduate course on intelligent wireless systems, with particular emphasis on cognitive networking algorithms, protocols and applications.
Class satisfies these areas for MS students only: systems, networking, application
Enrollment Code: 52126
Day and Time: MW 100-300
Location: Phelps 1401
Units: 4.0, letter grade ONLY
290N. Web Search and Mining
Instructor: Yang
This course covers advanced topics on Internet search, information retrieval and web mining. Topics include crawling, indexing, ranking, information classification and data mining. Students are expected to conduct a web search project.
Class satisfies these areas for MS students only: Applications.
Enrollment Code: 52142
Day and Time: M 1100-100 F 300-500
Location: Phelps 1401
Units: 4.0, letter grade ONLY
290H. Sparse Matrix Algorithms
Instructor: Gilbert
Description:
Sparse matrices are a basic tool of computational science and
engineering. They show up in applications ranging from models of the
physical world to web search and information retrieval. Using them
efficiently involves techniques from linear algebra, graph algorithms,
and computer architecture.
Sparse matrix algorithms are fascinating because they combine two languages that are often quite different, those of numerical computation and of graph theory. One result is that nobody knows it all -- there is always something new to be learned by trying to speak the language you're not expert in.
Most of the course will concern methods for solving large, sparse systems of linear equations. We will first study direct methods, which are based on Gaussian elimination and use tools from graph theory and discrete data structures. We will then study iterative methods, which treat the matrix as a black-box operator and use eigenvalues and eigenvectors to analyze convergence. Finally, we will study modern preconditioned methods, which combine the discrete structure of direct methods, the numerical structure of iterative methods, and the specifics of the problem domain.
The prerequisites are some knowledge of linear algebra (Gaussian elimination, eigenvalues and eigenvectors) and analysis of algorithms. I expect to have students with a variety of different backgrounds; if you have an application from a scientific or engineering field that includes solving a system of linear equations I encourage you to talk to me about the course.
Class satisfies these areas for MS students only: Theory and Applications
Enrollment Code: 52134
Day and Time: MW 1100-100
Location: Trailer 932
Units: 4.0, letter grade ONLY
