This lecture covers the concept of variational inference, focusing on the lower bound and Evidence Lower Bound (ELBO). It explains the Jensen's inequality, the E-step, and the M-step in the context of Mixure of Gaussians. The lecture also discusses the Markov chain Monte Carlo (MCMC) method for sampling from complex probability distributions.