EAS 5170 Mathematical Foundations for Machine Learning II: Linear Algebra (.5 CU)

Short Description

This course covers fundamental concepts in Linear Algebra that are useful in downstream courses in AI and data science. Topics covered include vector spaces, geometric notions such as inner products and norms, linear maps, and spectral theory, with each topic building upon and naturally leading to the next. While this is a short class, the presented material will be thoroughly covered and self-contained. This course is designed to provide students with a rigorous understanding of core Linear Algebra, concepts which are required to understand established and current data-driven methods. Students will leave the course with a solid understanding of Linear Algebra in its own right, as well as the ability to understand key mathematics underpinning machine learning.

Portfolio Building Course

No

Pre-Requisites

Basic understanding of 2D/3D vectors and matrices; solving systems of simultaneous equations, Gaussian elimination
Recommended: EAS 5160: Mathematical Foundations for Machine Learning I: Probability

Content

This course covers fundamental concepts in Linear Algebra that are useful in downstream courses in AI and data science. Topics covered include vector spaces, geometric notions such as inner products and norms, linear maps, and spectral theory, with each topic building upon and naturally leading to the next. While this is a short class, the presented material will be thoroughly covered and self-contained. This course is designed to provide students with a rigorous understanding of core Linear Algebra, concepts which are required to understand established and current data-driven methods. Students will leave the course with a solid understanding of Linear Algebra in its own right, as well as the ability to understand key mathematics underpinning machine learning.

Course Creators

• Santosh S. Venkatesh