Host: Holly Rushmeier
Title: Introduction to Linear Regression
In this lecture, we will introduce linear regression: motivating its use and covering a variety of techniques to learn its optimal parameters from training data. The focus will be on developing the intuitions behind the different optimization algorithms, and their advantages and disadvantages. We will end the lecture by briefly discussing the general trend of optimization in machine learning.
Sebastian Caldas is a Ph.D. candidate in the Machine Learning Department at Carnegie Mellon University, where he is advised by Artur Dubrawski. His research focuses on building intelligent systems that are collaborative, useful and practical. Currently, he is focused on federated scenarios, particularly healthcare domains.