ISYE 8803: Topics on High-Dimensional Data Analytics

Instructional Team

Kamran Paynabar

Kamran Paynabar
Creator, Instructor

Overview

This course focuses on analysis of high-dimensional structured data including profiles, images, and other types of functional data using statistical machine learning. A variety of topics such as functional data analysis, image processing, multilinear algebra and tensor analysis, and regularization in high-dimensional regression and its applications including low rank and sparse learning is covered. Optimization methods commonly used in statistical modeling and machine learning and their computational aspects are also discussed.

Course Goals

By the end of the course, you will:

  1. Learn machine learning and statistical methods for image processing and analysis of functional data.
  2. Learn a variety of regularization techniques and their applications.
  3. Be able to use multilinear algebra and tensor analysis techniques for performing dimension-reduction on a broad range of high-dimensional data.
  4. Understand how to use well-known optimization methods to create efficient learning algorithms.

Sample Syllabi

Summer 2020 syllabus and schedule (PDF)
Spring 2020 syllabus and schedule (PDF)

Note: Sample syllabi are provided for informational purposes only. For the most up-to-date information, consult the official course documentation.‚Äč

Before Taking This Class...

Suggested Background Knowledge

This class assumes knowledge of regression and linear algebra, as well as basic programming knowledge in R and Matlab.

Technical Requirements and Software
  • High-speed internet connection
  • Laptop or desktop computer with a minimum of a 2 GHz processor and 2 GB of RAM
  • Windows or Mac iOS
  • Complete Microsoft Office Suite or comparable, and ability to use Adobe PDF software (install, download, open, and convert)
  • Mozilla Firefox, Chrome, and/or Safari browsers
  • Matlab and R

Academic Integrity

All Georgia Tech students are expected to uphold the Georgia Tech Academic Honor Code. This course may impose additional academic integrity stipulations; consult the official course documentation for more information.