Graduate Course Proposal Form Submission Detail - ESI6636
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Approved by SCNS
Submission Type: New
Course Change Information (for course changes only):
Comments: for MSIE, PhD in IE, MSEM - Elective; pending clarification of desc (included pre-reqs). Emailed 2/11/15. Confirmed. GC appd 2/11/15. To USF Sys 2/27/15. Nmbr 6609 apprd as 6636. Effective 4/1/15
- Department and Contact Information
Tracking Number Date & Time Submitted 5124 2014-10-22 Department College Budget Account Number EN 210300 Contact Person Phone Tapas Das 9745585 email@example.com
- Course Information
Prefix Number Full Title ESI 6636 Advanced Analytics II Is the course title variable? N Is a permit required for registration? N Are the credit hours variable? N Is this course repeatable? Y If repeatable, how many times? 1 Credit Hours Section Type Grading Option 3 C - Class Lecture (Primarily) R - Regular Abbreviated Title (30 characters maximum) Advanced Analytics II Course Online? Percentage Online C - Face-to-face (0% online) 0
Covers broad aspects of the emerging field of data analytics, with focus on statistical learning and predictive modeling methods. Basic knowledge in probability & statistical methods and linear algebra rqd. Prior programming experience a plus.
A. Please briefly explain why it is necessary and/or desirable to add this course.
Replacing Selected Topics with Permanent number; already listed in program
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?
Anticipated enrollment based on prior years is between 8-15 students.
C. Has this course been offered as Selected Topics/Experimental Topics course? If yes, how many times?
Yes, 3 or more times
D. What qualifications for training and/or experience are necessary to teach this course? (List minimum qualifications for the instructor.)
A PhD in Industrial Engineering or a related field is required to teach this course.
- Other Course Information
1) Learn the basic techniques of statistical learning and predictive modeling;
2) Learn the principles of building and validating these modern statistical models for engineering applications;
3) Demonstrate your knowledge of the techniques and principles by implementing existing techniques or developing a novel technique in a real world problem, and conduct a comprehensive and insightful review study in any emerging area of statistical learning and data mining.
B. Learning Outcomes
After successful completion of this course, students will be able to:
-understand the basic regression, classification and clustering models in literature;
-know how to select the best models; -apply analytics models in their research.
C. Major Topics
Regression: linear regression, nonlinear and nonparametric regression (KNN regression, kernel regression, Gaussian process), additive models, local linear regression, conditional variance regression model
Classification: logistic regression, linear discriminant analysis, support vector machine
Tree-based methods: CART, boosting and MART, bagging and random forest
Model selection: overfitting, bias-variance decomposition, cross validation, bootstrap
System modeling: kernel density estimation, graphical models
System identification: latent variable models, clustering, mixture models (Gaussian mixture and EM algorithm)
High-dimensional learning techniques: feature selection, dimension reduction, sparse learning
Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning, 2nd Ed. Springer Series in Statistics.
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. An Introduction to Statistical Learning with Applications in R, 1st Ed. Springer New York, 2013.
E. Course Readings, Online Resources, and Other Purchases
F. Student Expectations/Requirements and Grading Policy
Grading Scale: A = 90-100%, B = 80-90%, C = 70-80%, D = 60-70%, F = below 60%
Grading Weight: Homework (20%), Mid-term Exam (25%), Final Exam (25%), Project (30%)
No make-up exams unless previous arrangements have been made. Students will be expected to attend class and prepare assignments. Habitual failure to do so will result in a reduced grade. An incomplete grade will only be given when a student misses a portion of the semester because of illness or accident. Cheating on examinations, plagiarism and other forms of academic dishonesty are serious offenses and may subject the student to penalties ranging from failing grades to dismissal.
G. Assignments, Exams and Tests
H. Attendance Policy
Course Attendance at First Class Meeting Policy for Graduate Students: For structured courses, 6000 and above, the College/Campus Dean will set the first-day class attendance requirement. Check with the College for specific information. This policy is not applicable to courses in the following categories: Educational Outreach, Open University (TV), FEEDS Program, Community Experiential Learning (CEL), Cooperative Education Training, and courses that do not have regularly scheduled meeting days/times (such as, directed reading/research or study, individual research, thesis, dissertation, internship, practica, etc.). Students are responsible for dropping undesired courses in these categories by the 5th day of classes to avoid fee liability and academic penalty. (See USF Regulation Registration - 4.0101,
Attendance Policy for the Observance of Religious Days by Students: In accordance with Sections 1006.53 and 1001.74(10)(g) Florida Statutes and Board of Governors Regulation 6C-6.0115, the University of South Florida (University/USF) has established the following policy regarding religious observances: (http://usfweb2.usf.edu/usfgc/gc_pp/acadaf/gc10-045.htm)
In the event of an emergency, it may be necessary for USF to suspend normal operations. During this time, USF may opt to continue delivery of instruction through methods that include but are not limited to: Blackboard, Elluminate, Skype, and email messaging and/or an alternate schedule. Its the responsibility of the student to monitor Blackboard site for each class for course specific communication, and the main USF, College, and department websites, emails, and MoBull messages for important general information.
I. Policy on Make-up Work
No make-up exams unless previous arrangements have been made. Cheating on examinations, plagiarism and other forms of academic dishonesty are serious offenses and may subject the student to penalties ranging from failing grades to dismissal.
J. Program This Course Supports
PhD and MS in Industrial Engineering
- Course Concurrence Information
This course is also applicable to other engineering disciplines at the graduate level and also Healthcare and business graduate programs.