Graduate Course Proposal Form Submission Detail - COP5730
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- Department and Contact Information
Tracking Number Date & Time Submitted 1815 2005-11-02 Department College Budget Account Number Computer Science and Engineering EN 2108 Contact Person Phone Lawrence Hall 9744195 email@example.com
- Course Information
Prefix Number Full Title COP 5730 Data Mining Is the course title variable? N Is a permit required for registration? N 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) Data Mining Course Online? Percentage Online -
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.
A. 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
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 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.
C. Has this course been offered as Selected Topics/Experimental Topics course? If yes, how many times?
D. 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.
- Other Course Information
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.
B. 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.
C. Major Topics
Supervised learning (including decision trees, neural networks and support vector machines)
Unsupervised learning(including clustering and association rule mining)
Working with class skewed data sets and very large, potentially noisy data sets
Data Mining} by Ian H. Witten and Eibe Frank, second edition, Morgan Kaufmann Publishers, CA., 2005.
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
- Course Concurrence Information