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Vision + Focus + Execution

Vision + Focus + Execution. Meiliu Lu, RVR 5016, mlu@csus.edu http://gaia.ecs.csus.edu/~mei/ For CSc 209 Spring 2003, 5/6/03. Outline. Vision : know what is going to happen Focus : Interests – key elements to keep you going, select project topic Execution : methodology

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Vision + Focus + Execution

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  1. Vision + Focus + Execution Meiliu Lu, RVR 5016, mlu@csus.edu http://gaia.ecs.csus.edu/~mei/ For CSc 209 Spring 2003, 5/6/03

  2. Outline • Vision: know what is going to happen • Focus: Interests – key elements to keep you going, select project topic • Execution: methodology • Take a look at how others have done • Prepare, start, and finish yours

  3. R. W. Hamming on R & D • What can happen  Science • What will happen  engineering & economics • What should happen  morals, ethics and including what society is prepared to accept and reject

  4. Richard M. Karp’s 3 Principles on Making Career Decisions • Understand what you are good at and what you like to do, and choose accordingly. In the words of Socrates, “Know thyself.” • Disregard the fashions of the day and search for new areas of research that are about to become important. In the words of the great hockey player and philosopher Wayne Gretzky, “Skate to where the puck is gonna be.” • To find exciting problems, look at the interfaces between disciplines.

  5. Research Interests and Projects • Machine learning and data mining • DMV Data mining (clustering and data warehousing, OLAP) • Data management and integration • Web portal development (Searchware) • Data integration and visualization (multiple data sources and single analytical view) • Courseware Development • XML course module (136) • Data Warehousing course module (196K) • SVM, support vector machine (196K, 219)

  6. Select Project Topic • Select a topic according to your interest and factors such as following: • Utility vs. innovation • Learning • Timing

  7. What is Data Mining? • Data mining, simulation, and modeling are part of a large process – Knowledge Discovery in Data (KDD) • Finding hidden information in a database • Fit data to a model • Web related data • Business, health, and financial data • Scientific and Engineering

  8. Data Mining Algorithms • Objective: fit data to a model • Descriptive • Predictive • Preference – technique to choose the best model • Search – technique to search the data • “Query”

  9. Database Find all credit applications with last name of smith Identify customers who have purchased more than $10,000 in the last month. Find all customers who have purchased milk Data mining Find all credit applicants who are poor credit risks. (classification) Identify customers with similar buying habits. (clustering) Find all item which are frequently purchased with milk. (association rules) Query Examples

  10. Extracting Knowledge from Gene Expression Data: A Case Study of Batten Disease – S. M. Lin • Duke University Medical Center proposed a prototype KDD system to enable scientists to analyze the massive microarray data, form hypotheses, and draw insights directly into underlying mechanisms of diseases. • Data  Microarray database  data mining  patterns  human experts  Genomics knowledge base discoveries

  11. Your MS Project • Try to make someone happy • yourself • people who helped you • people who need help • Start early, keep working, finish on time

  12. Sample MS Project 1 • Jeffery Lewis, Spring 2001, “Audio Compression for Music Synthesis Using Gaussion Functions and Genetic Algorithms” • Paper published in the proceedings of 3rd International Conference on Information Reuse and Integration. November 2001 • Self-selected topic

  13. Sample MS Project 2 • Yan Xiong, Summer 2002, “A Web-Based Microarray Management System” • Paper published inthe 15th International Conference on Computer Applications in Industry and Engineering. November 2002. • Topic introduced at CSC 209 Fall 2001 • Improvement: engineering and function

  14. Sample MS Project 3 • Wen Xia, Fall 2002, “Online Course Management System” • Developed for C Sc 138 and can be adopted for other courses • Topic selected by self (continue development of an existing tool) • More system enhancement can be a new MS project

  15. Writing Report • “MS Thesis and Project Writing Guide” at: http://gaia.ecs.csus.edu/~mei/ • Format, References and Acknowledgement • A useful reference book: “Writing for Computer Science” by Justin Zobel, Springer, 1998. • Game of time and stepping stone

  16. Leaders are VisionariesDr. Ernie Bodai • They have a poorly developed sense of fear • They have no concept of the odds against them • They make the impossible happen

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