Probabilistic Machine Learning

Learning Goals

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.

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

B

Curriculum Industry
TAF Type

B

Curriculum Health
TAF Type

B

Curriculum Economy
TAF Type

B

SSD

INF/01

ECTS

6

Semester

2

Lecturers

Luca Bortolussi