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Graduate Course Proposal Form Submission Detail - ESI6638
Tracking Number - 1764

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Current Status: Approved, Permanent Archive - 2006-05-05
Submission Type:
Course Change Information (for course changes only):

Detail Information

  1. Date & Time Submitted: 2006-04-10
  2. Department: Industrial and Management Systems Engineering
  3. College: EN
  4. Budget Account Number: 2103-000-00
  5. Contact Person: Qiang Huang
  6. Phone: 45579
  7. Email:
  8. Prefix: ESI
  9. Number: 6638
  10. Full Title: Engineering 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?: Y
  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): Eng Data Mining
  19. Course Online?: -
  20. Percentage Online:
  21. Grading Option: R - Regular
  22. Prerequisites: ESI 6247 Statistical Design Models or equivalent
  23. Corequisites:
  24. Course Description: The course will present the theory and methods of data mining, with emphasis on applications in engineering. The topics include linear models, classification, smoothing and kernel methods, model selection and inference, and support vector machines, etc.

  25. Please briefly explain why it is necessary and/or desirable to add this course: With the advance of sensors and information technology, extensive amount of data has been generated in manufacturing and service systems. Effective exploration, modeling, and prediction based on the data are critical to control and improve systems perform
  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 serves as graduate-level elective for both Master and Ph.D. students in IMSE department. It is also expected to serve other graduates in the College of Engineering.
  27. Has this course been offered as Selected Topics/Experimental Topics course? If yes, how many times? Yes, two (2) times.
  28. What qualifications for training and/or experience are necessary to teach this course? (List minimum qualifications for the instructor.) A Ph.D. degree in Industrial Engineering, or in other technical disciplines such as Statistics or Electrical Engineering, plus experience in a relevant technical environment with expertise in applied statistical data analysis.
  29. Objectives: The course objective is to introduce the statistical theory and methods of data mining, with emphasis on applications in engineering.
  30. Learning Outcomes: The students will: (1) understand important ideas of data mining in the fields of Statistics, and spawned new areas such as data mining, machine learning and bioinformatics under a common conceptual framework, (2) learn theories and tools used in data mining, (3) learn computational software for implementing data mining approaches, (4) learn problem definition and solving through course project.
  31. Major Topics: The tentative topics include supervised learning, linear method for regression, classification, smoothing and kernel methods, model selection and inference, and support vector machines and flexible discrimination.
  32. Textbooks: “The Elements of Statistical Learning”by Hastie, Tibshirani and Friedman (2001). Springer-Verlag.
  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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