Explore unsupervised learning and autoencoding techniques in this comprehensive 57-minute lecture. Delve into topics such as generative models, input and latent space interpolation, conditional generative networks, and style transfer. Learn about super resolution, inpainting, and caption-to-image generation using Dall-e. Understand key concepts like energy-based models, reconstruction energies, and loss functionals. Examine various autoencoder architectures, including denoising, nearest neighborhood, and sparse autoencoders. Gain insights into under and over-complete hidden layers, and conclude with final remarks on the subject.