Introduces Scientific Machine Learning, emphasizing its application in various scientific fields and the connection between machine learning and physics.
Introduces Lasso regularization and its application to the MNIST dataset, emphasizing feature selection and practical exercises on gradient descent implementation.
Explores the nearest neighbor classifier method, discussing its limitations in high-dimensional spaces and the importance of spatial correlation for effective predictions.
