Learning how to deal with complex data sets and build predictors and classifiers, know and being able to use state of the art probabilistic machine learning approaches for supervised, unsupervised and self-supervised learning, combine different methods to improve results.
Probabilistic Machine Learning
Learning Goals
Program in pills
Graphical models; inference; Bayesian regression and classification; Kernel based methods; Approximate inference for models with latent variables.
Area
Machine Learning and Artificial Intelligence
Curriculum Foundations
TAF Type
Curriculum Industry
TAF Type
Curriculum Health
TAF Type
Curriculum Economy
TAF Type
SSD
ECTS
Semester
Lecturers
Luca Bortolussi