Machine Learning Review

This lecture provides a review of machine learning concepts, starting with supervised learning and the stages of training and testing. It covers classification vs regression, linear models, linear regression, and multi-output models. The lecture delves into kernel functions, support vector machines, and handling overlapping classes. It also explores dimensionality reduction techniques like PCA, LDA, and t-SNE, as well as deep generative models and autoencoders. The discussion extends to unsupervised learning methods such as mixture models, LDA, and GANs, concluding with data representation and cross-validation strategies.