Learning Probabilistic Graphical Models from Data

Extracting meaningful patterns from complex data requires more than just simple statistics. This course provides a clear path to understanding how Probabilistic Graphical Models (PGMs) can be learned directly from data to represent intricate relationships between variables. You will gain the skills to transform raw datasets into structured probabilistic representations that can be used for prediction and decision-making in fields ranging from medicine to robotics. What you'll learn: - Understand the fundamental principles of parameter estimation in Bayesian and Markov networks. - Apply Maximum Likelihood Estimation and Bayesian techniques to learn from complete and incomplete datasets. - Discover the structure of graphical models using score-based and constraint-based algorithms. - Learn how to handle latent variables using modern approaches like the Expectation-Maximization algorithm. - Practice evaluating model quality and complexity to avoid overfitting in high-dimensional domains. The course starts with essential terminology and the core goals of the learning task, then progresses through parameter estimation, structure learning, and handling hidden data. It is designed for beginners interested in machine learning and data science, requiring no previous background in graphical models. Start building smarter models by learning the structure of uncertainty today.