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Graduate Course Proposal Form Submission Detail - COP5730
Tracking Number - 1815

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Current Status: Approved, Permanent Archive - 2006-01-15
Submission Type:
Course Change Information (for course changes only):

Detail Information

  1. Date & Time Submitted: 2005-11-02
  2. Department: Computer Science and Engineering
  3. College: EN
  4. Budget Account Number: 2108
  5. Contact Person: Lawrence Hall
  6. Phone: 9744195
  7. Email:
  8. Prefix: COP
  9. Number: 5730
  10. Full Title: Data Mining
  11. Credit Hours: 3
  12. Section Type: C - Class Lecture (Primarily)
  13. Is the course title variable?: N
  14. Is a permit required for registration?: N
  15. Are the credit hours variable?: N
  16. Is this course repeatable?:
  17. If repeatable, how many times?: 0
  18. Abbreviated Title (30 characters maximum): Data Mining
  19. Course Online?: -
  20. Percentage Online:
  21. Grading Option: R - Regular
  22. Prerequisites: Undergraduate Statistics
  23. Corequisites:
  24. Course Description: An introductory course to mining information from data. Scalable supervised and unsupervised machine learning methods are discussed. Methods to visualize and extract heuristic rules from large databases with minimal supervision is discussed.

  25. Please briefly explain why it is necessary and/or desirable to add this course: Data mining covers a large portion of the machine learning area of pattern recognition. Many companies are utilizing mining to discover interesting facts held in databases. There is a large market for people who know about statistics and data mining. T
  26. What is the need or demand for this course? (Indicate if this course is part of a required sequence in the major.) What other programs would this course service? This course has been run about five times and always gotten an enrollment of between 20 and 40 graduate students. There is significant demand from within the department from people doing research in artificial intelligence or Computer Vision robotics. Students from statistics, business, and other engineering departments may also have an interest and have taken the class.
  27. Has this course been offered as Selected Topics/Experimental Topics course? If yes, how many times? Four times.
  28. What qualifications for training and/or experience are necessary to teach this course? (List minimum qualifications for the instructor.) A background in statistics or artificial intelligence, with some specialization in pattern recognition/machine learning.
  29. Objectives: o Understand how to build models of data sets.

    o Understand how to intelligently analyze data and interpret models of data.

    o Understand supervised machine learning.

    o Understand association rules and clustering for use when data lacks labels.

  30. Learning Outcomes: The students will have the ability to use data mining tools such as WEKA and those embedded in SASS. They will be able to build new machine learning algorithms or modify current ones for data mining. They will be able to differentiate between approaches and understand how to set up experiments to build models of data.
  31. Major Topics: Knowledge representation

    Supervised learning (including decision trees, neural networks and support vector machines)

    Unsupervised learning(including clustering and association rule mining)

    Ensemble classifiers

    Working with class skewed data sets and very large, potentially noisy data sets

  32. Textbooks: Data Mining} by Ian H. Witten and Eibe Frank, second edition, Morgan Kaufmann Publishers, CA., 2005.
  33. Course Readings, Online Resources, and Other Purchases:
  34. Student Expectations/Requirements and Grading Policy:
  35. Assignments, Exams and Tests:
  36. Attendance Policy:
  37. Policy on Make-up Work:
  38. Program This Course Supports:
  39. Course Concurrence Information:

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