Lecture
This lecture covers the fundamentals of linear regression within the context of machine learning. It begins with a recap of machine learning components, including data, algorithms, and insights. The instructor discusses the types of data used in linear regression, such as patient information for predicting birth weight, and the importance of understanding the relationship between input features and output predictions. The lecture introduces the concept of fitting a line to noisy data, explaining the training phase of linear regression, where the goal is to find optimal parameters that minimize prediction errors. The instructor elaborates on extending linear regression to multiple dimensions, demonstrating how to handle multi-dimensional inputs and outputs. Key mathematical concepts, including derivatives and gradients, are reviewed to derive the solution for linear regression. The lecture concludes with an introduction to evaluation metrics for regression models, emphasizing the importance of assessing model performance on unseen data. Examples, including wine quality prediction and author age estimation, illustrate the practical applications of linear regression.