Explore variational autoencoders in this 43-minute lecture from Northeastern University's CS 7150 Deep Learning course. Delve into generative models, plain autoencoders, variational and evidence lower bounds, variational autoencoder architecture, and stochastic optimization techniques. Access comprehensive notes and references, including works by Kingma and Welling, to deepen your understanding of this advanced machine learning topic. Gain insights into latent codes, likelihood optimization, noisy observation models, KL divergence, regularization, and multistep optimization processes in the context of variational autoencoders.