Probabilistic Graphical Models 3: Learning
This course is the third in a sequence of three. Following the first course, which focused on representation, and the second, which focused on inference, this course addresses the question of learning: how a PGM can be learned from a data set of examples. The course discusses the key problems of parameter estimation in both directed and undirected models, as well as the structure learning task for directed models. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of two commonly used learning algorithms are implemented and applied to a real-world problem.
- Fecha Incio:27/01/2020
- Idioma: Inglés
- Universidad: Stanford University
- Profesores: Daphne Koller
- Certificado: No