Machine Learning Graphical Models Bayesian Networks, examples, conditional independence, inference Marc Toussaint FU Berlin The need for modelling • Given a real world problem, translating it to a well-defined learning problem is non-trivial. • The “framework” of plain regression/classification is rather restricted: input x, output y. • Graphical models (probabilstic models with multiple random variables and dependencies) are a more general framework for modelling “problems”; regression & classification become a special case; Reinforcement Learning, decision making, but also language processing, image segmentation, are special cases. 2/21 Graphical Models • The core difficulty in modelling is specifying What are the relevant variables? How do they depend on each other? (Or how could they depend on each other→ learning) • Graphical models are a simple, graphical notation for 1) which random variables exist 2) which random variables are “directly coupled” Thereby
