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.
This course is not foundational and does not count toward any specializations at present, but it can be counted as a free elective.
By the end of the course, you will:
- Learn machine learning and statistical methods for image processing and analysis of functional data.
- Learn a variety of regularization techniques and their applications.
- Be able to use multilinear algebra and tensor analysis techniques for performing dimension-reduction on a broad range of high-dimensional data.
- Understand how to use well-known optimization methods to create efficient learning algorithms.
Spring 2023 syllabus and schedule (PDF)
Fall 2022 syllabus and schedule (PDF)
Summer 2022 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.
Before Taking This Class...
Suggested Background Knowledge
This class assumes knowledge of regression and linear algebra, as well as basic programming knowledge in Python, R, or 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
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.