This lecture delves into the statistical view of generative models, exploring the likelihood of data under a model, illustrated through examples like the Gaussian distribution. It explains the concept of maximum likelihood, emphasizing the importance of choosing model parameters that maximize the log-likelihood, and concludes with a summary of the maximum likelihood method.