Dive into the world of unsupervised learning through a comprehensive 4.5-hour tutorial covering a wide range of advanced topics. Explore Gaussian Mixture Models, clustering techniques like K-means and Hierarchical clustering, and Principal Component Analysis (PCA). Discover the inner workings of recommendation systems using Matrix Factorization, and delve into Latent Dirichlet Allocation with a two-part explanation including Gibbs Sampling. Gain insights into Restricted Boltzmann Machines, learn about Singular Value Decomposition and its application in image compression, and understand Denoising and Variational Autoencoders. Conclude with a friendly introduction to Generative Adversarial Networks (GANs), equipping yourself with cutting-edge knowledge in unsupervised learning techniques.