Learn about probabilistic graphical models and Bayesian networks in this university lecture covering essential concepts like exponential and Gaussian likelihood, maximum likelihood estimation, and network structures. Explore the mathematical foundations of expectations, base rules, and graph representations while understanding how to work with real data. Dive into linear Gaussian models, conditional independence, and network cycles to gain a comprehensive understanding of probabilistic modeling techniques used in data science and machine learning applications.