IE 529
IE 529 - Stats of Big Data & Clustering
Fall 2016
| Title | Rubric | Section | CRN | Type | Hours | Times | Days | Location | Instructor |
|---|---|---|---|---|---|---|---|---|---|
| Stats of Big Data & Clustering | IE529 | A | 66823 | LCD | 4 | 1300 - 1350 | M W F | 153 Mechanical Engineering Bldg | Carolyn L Beck |
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Official Description
This course will cover various foundational topics in data science. Parametric and non-parametric methods. Hypothesis testing; Regression; Classification; Dimension reduction; and Regularization. Unsupervised and semi-supervised learning, along with a few case studies. Course Information: 4 graduate hours. No professional credit. Prerequisite: MATH 415 and IE 300 or equivalent. All ISE graduate students and students enrolled in the Master of Science in Advanced Analytics (MSAA) are eligible to take the course.