Structured prediction refers to a type of supervised machine learning where the goal is to predict structured objects instead of single values. These models are trained using observed data and adjust parameters based on the true prediction value. However, due to the complexity of the model and interrelations between variables, prediction and training can be computationally challenging, requiring approximate inference and learning methods.
Stanford University
Winter 2023
An in-depth study of probabilistic graphical models, combining graph and probability theory. Equips students with the skills to design, implement, and apply these models to solve real-world problems. Discusses Bayesian networks, exact and approximate inference methods, etc.
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