Linear Classification: Signal Processing

This lecture covers linear classification in signal processing, focusing on solving least-squares problems, the need for regularization, and the inclusion of bias terms in linear predictions. It also delves into feature extraction for fMRI classification, the structure of linear classifiers, loss functions, cross-validation techniques, and the popularity of linear classifiers in fMRI analysis. The instructor discusses the spatial scales exploited by multivariate pattern analysis (MVPA) and the hyperacuity hypothesis, which explores fine-grained spatial details in fMRI data.