Machine Learning: Brain Imaging and Classifier Principles

This lecture by the instructor covers the application of machine learning in functional brain imaging, focusing on the principles of brain signal encoding and decoding. Topics include linear classifiers, generative versus discriminative models, support vector machines, and the kernel trick. Emphasis is placed on understanding the spatial patterns of brain activity related to emotions and the importance of classifier specificity-sensitivity trade-offs. The lecture also delves into model complexity, overfitting, and the decoding of images and video sequences using advanced machine learning techniques.