| Table of Contents |
|---|
I. Welcome Video |
II. Course Overview at a Glance |
III. Course Objectives |
IV. Course Description |
V. Tentative Schedule |
VI. Sample Lecture |
I. Welcome Video
Videos removed on print.
Jump to top
II. Course Overview at a Glance
| Time & Place | Tuesday, Thursday: 10:00am – 11:15am, Wood Building, Room WG-62 |
| Instructors | Abdus Sattar, PhD |
| Office | BRB: Suite G-19 |
| Office Hours | Tuesday and Thursday 11:15am – 12:15 noon or by appointment |
| Course Web Page | canvas.case.edu |
| Textbook (Required) | Modern Multivariate Statistical Techniques by Alan J. Izenman (ISBN:978-0-387-78188-4) |
| Reference | Data mining for genomics and proteomics by Darius M. Dziuda (ISBN: 978-0-470-16373-3) |
Prerequisites:
|
|
| Disability Help If you have a disability and need help, please contact me and the Office of Educational Support Services at disability@case.edu, 216.368.5230 as early as possible in the term. |
|
| Academic Integrity You are expected to maintain the highest integrity in your work for this class. This includes not passing off anyone else’s work as your own, even with their permission. Your homework solutions must be your own work, not from outside sources, consistent with the university rules on academic honesty. I expect you to follow this policy scrupulously. Evidence of academic dishonesty may lead to loss of credit for the assignment, and possibly failure of the course. |
|
II. Course Objectives
- Gain proficiency in multivariate and machine learning methods.
- Hone skills by applying text book tools in solving real-world data analysis problems.
- Gain competency in standard and cutting edge multivariate methods and algorithms.
III. Course Description
Contemporary multivariate analysis, including statistical learning and inference methods when the number of measures far exceeds the number of subjects (“high-dimensional data”). Topics include (but not limited to) classical modeling and inference under multivariate normal theory, principal components, descriptive and confirmatory factor analysis, partial least squares, classification and supervised learning, cluster analysis and unsupervised learning methods. This course stresses how the core modeling principles, computing tools, and visualization strategies are used to address complex scientific aims powerfully and efficiently, and to communicate those findings effectively to content researchers who may have little or no experience in these methods.
IV. Tentative Schedule
VI. Sample Lecture
Videos removed on print.
Jump to top
Materials
PDF preview removed on print.
Jump to top