Matrix Calculus for Machine Learning and Beyond

Nov 5, 2023 ·

Matrix calculus is an essential tool for machine learning and beyond. It enables us to obtain and analyze data by making predictions, designing algorithms, and optimizing parameters. It also helps us to interpret the results of machine learning, as well as to develop further applications of it.

The MIT course 18-S096 Matrix Calculus for Machine Learning and Beyond (January IAP 2023) introduces students to the fundamentals of matrix calculus and its applications in machine learning and beyond. Through this course, students learn how to use matrices to solve various problems in machine learning and other related fields.

The topics covered in this course include basic linear algebra and matrix calculus, basic optimization techniques, and their applications in supervised and unsupervised learning. The main goal of the course is to understand the mathematical foundations of matrix calculus and its applicability in machine learning and beyond. In addition, this course introduces several advanced topics related to linear algebra and optimization, such as gradient descent, convex optimization, SVD, and eigenvalues.

This course is designed for students who are interested in developing a deeper understanding of machine learning, as well as those who want to apply the techniques of matrix calculus to other areas. It provides a comprehensive overview of matrix calculus and its applications in machine learning and beyond. By the end of the course, students will be able to apply the concepts learned to their own projects and research.