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Graduate Course Proposal Form Submission Detail - ESI6638

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Current Status: Approved, Permanent Archive - 2006-05-05
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  1. Department and Contact Information

    Tracking Number Date & Time Submitted
    1764 2006-04-10
     
    Department College Budget Account Number
    Industrial and Management Systems Engineering EN 2103-000-00
     
    Contact Person Phone Email
    Qiang Huang 45579 huangq@eng.usf.edu

  2. Course Information

    Prefix Number Full Title
    ESI 6638 Engineering Data Mining

    Is the course title variable? N
    Is a permit required for registration? Y
    Are the credit hours variable? N
    Is this course repeatable?
    If repeatable, how many times? 0

    Credit Hours Section Type Grading Option
    3 C - Class Lecture (Primarily) R - Regular
     
    Abbreviated Title (30 characters maximum)
    Eng Data Mining
     
    Course Online? Percentage Online
    -

    Prerequisites

    ESI 6247 Statistical Design Models or equivalent

    Corequisites

    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.


  3. Justification

    A. 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

    B. 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.

    C. Has this course been offered as Selected Topics/Experimental Topics course? If yes, how many times?

    Yes, two (2) times.

    D. 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.


  4. Other Course Information

    A. Objectives

    The course objective is to introduce the statistical theory and methods of data mining, with emphasis on applications in engineering.

    B. 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.

    C. 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.

    D. Textbooks

    “The Elements of Statistical Learning”by Hastie, Tibshirani and Friedman (2001). Springer-Verlag.

    E. Course Readings, Online Resources, and Other Purchases

    F. Student Expectations/Requirements and Grading Policy

    G. Assignments, Exams and Tests

    H. Attendance Policy

    I. Policy on Make-up Work

    J. Program This Course Supports


  5. Course Concurrence Information



- if you have questions about any of these fields, please contact chinescobb@grad.usf.edu or joe@grad.usf.edu.