Graduate classes, Spring 2011, Computer Science

CS 526: AlgorithmsCredits: 4− Description− Sections
Content: This course is a graduate level introduction to the design and analysis of algorithms. Although we will review some undergraduate level material, we will instead emphasize reading and experimentation at a level appropriate for the initiation of research. This course will have both theoretical and practical content. As course highlights, students will be expected to implement and analyze the performance of a fundamental data structure, starting with a close reading of the original research paper.
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Assessments: There will be about six regular homework assignments, including both written and programming components, counting for at least half of the grade. There will be a take-home mid-term exam and a take-home final exam. The examinations will emphasize both analysis and reading.
Prerequisites: CS 224 and CS 253.
000MSC: W301MWF 9:35am - 10:25amMichelangelo Grignimax 25
CS 555: Parallel ProcessingCredits: 4− Description− Sections
Content: Principles of parallel and concurrent processing. Algorithm classes including sorting, graph algorithms, matrix computations, alpha-beta search, fourier transforms, and numercial analysis. Study of parallel architectures and concurrent computing models. Assignments include programming distributed and shared memory multiprocessors, cluster systems, and analysis of performance and speedup curves.
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Prerequisites: CS255, CS351.
000MSC: W302TuTh 10:00am - 11:15amVaidy Sunderammax 30
CS 570: Data MiningCredits: 4− Description− Sections
Content: This course offers an introduction to data mining concepts and techniques. The goal is for the students to have a solid foundation in data mining that allows them to apply data mining techniques to real-world problems and to conduct research and development in new data mining methods. Topics include data processing, design and implementation of data warehouse and OLAP systems, data mining algorithms and methods including association analysis, classification, cluster analysis, as well as emerging applications and trends in data mining.
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Prerequisites: Some familiarity with a programming language, such as Java or C++, is required for programming assignments and/or final project. Some knowledge about database systems and statistics will be helpful.
000MSC: W302TuTh 1:00pm - 2:15pmLi Xiongmax 25
CS 580: Operating SystemsCredits: 4− Description− Sections
Content: The structure and organization of computer operating systems. Process, memory, and I/O management; device drivers, inter-machine communication, introduction to multiprocessor systems. An important portion of the course is a course long programming project that implements a simple operating system in stages. Each stage takes about three weeks, and is used as a basis for the next stage.
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000MSC: E408TuTh 2:30pm - 3:45pmKen Mandelbergmax 16
CS 584: Topics in Computer Science: Semantic WebCredits: 4− Description− Sections
Content: The semantic web is primarily an approach to leveraging content metadata for an improved, \intelligent" web experience. The course will explore the role metadata plays in the world wide web via supporting models, tools and technologies: RDF (Re- source Description Framework), OWL (Web Ontology Language), XML, relational data, and more. We will look at the limitations of HTML, and discuss the role that browsers play in supporting the web. Finally, we will look at some of the algorithms that contribute to the semantic web.
Texts: Programming The Semantic Web by Toby Segaran and Colin Evans (O'Reilly Media; July 2009). ISBN: 0596153813 (Required Text). Algorithms Of The Intelligent Web by Haralambos Marmanis and Dmitry Babenko (Manning Publications; June 2009). ISBN: 1933988665 (Optional Text).
Assessments: The course will include web development projects, homework, presentations, and 2 exams (mid-term and final). Graduate standing required.
Prerequisites: TBA
000MSC: W302TuTh 4:00pm - 5:15pmJoan Smithmax 25
CS 584R: Topics in Computer Science: Exascale Data AnalyticsCredits: 4− Description− Sections
Content: This course will cover techniques used to perform complex analysis of multiscale, multidimensional structured and unstructured information at exascale data volumes. Topics to be covered include: high end I/O, parallel filesystems, data mining, in transit data processing, system architectures that provide particular support for high end data analytics, memory hierarchies for data analytics, application use cases.
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001Psych 561F 12:00pm - 2:40pmJoel Saltzmax 15
CS 597R: Directed StudyCredits: 1 - 12− Description− Sections
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CHEUMSC: -----Shun Yan Cheungmax 999
LUJames Lumax 999
NAGYJames Nagymax 999
TIRAFaculty (TBA)max 999
XIONLi Xiongmax 999
CS 598R: Rotation ProjectCredits: 1 - 4− Description− Sections
Content: Computer Science and Informatics PhD students are required to complete two rotation projects prior to their qualifying exams and dissertation research. Projects often involve interdisciplinary work, and can be co-supervised by a Math/CS faculty and an external faculty member or researcher (e.g., Schools of Medicine and Public Health, the CDC). Students are required to submit a project proposal and a final report.
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AGICMSC: -----Eugene Agichteinmax 999
GRIGMichelangelo Grignimax 999
LUJames Lumax 999
NAGYJames Nagymax 999
SUNDVaidy Sunderammax 999
XIONLi Xiongmax 999
CS 599R: Master's Thesis ResearchCredits: 1 - 12− Description− Sections
Content: TBA
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AGICMSC: -----Eugene Agichteinmax 999
GRIGMichelangelo Grignimax 999
LUJames Lumax 999
QINFaculty (TBA)max 999
TAYLJames Taylormax 999
XIONLi Xiongmax 999
CS 700R: Graduate SeminarCredits: 1− Description− Sections
Content: This is a required course for all students in the PhD program. It comprises seminars given by faculty, invited guests, and students.
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000MSC: W301F 3:00pm - 4:00pmJames Lumax 35
CS 730R: Topics in Data & Information Management: Advanced Database SystemsCredits: 4− Description− Sections
Content: The course Advanced Database Systems covers recent advances in DBMSs and their application to biomedical problems. The topics include extensions and extensibility of database systems, XML databases, spatial databases, temporal databases, biomedical data management and integration, semantics enabled data management, parallel and distributed databases. The course comes with projects from biomedical research environments, with real data and databases. Students will get hands-on research and development experience from these projects.\\ \\ Web: https://web.cci.emory.edu/confluence/display/CS730R
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Prerequisites: Basic data structures and database background (CS 377 or equivalent), Familiar with a programming language (e.g., Java) preferred but not required, or permission of the instructor.
000MSC: W303MW 4:00pm - 5:15pmFusheng Wangmax 25
CS 797R: Directed StudyCredits: 1 - 12− Description− Sections
Content: TBA
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00PMSC: -----Faculty (TBA)max 999
GRIGMichelangelo Grignimax 999
CS 799R: Dissertation ResearchCredits: 1 - 12− Description− Sections
Content: TBA
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00PMSC: -----James Lumax 999
AGICEugene Agichteinmax 999
MANDKen Mandelbergmax 999
NAGYJames Nagymax 999
XIONLi Xiongmax 999