Who: Scanagatta Mauro, Ph.D. Candidate @ IDSIA 

Title: Advancements in Bayesian network structure learning

Abstract: A Bayesian network (BN) is a versatile and well-known probabilistic graphical model with applications in a variety of fields.
Their graphical nature makes BNs excellent models for representing the complex probabilistic relationships existing in many real problems ranging from bioinformatics to law, from image processing to economic risk analysis.
In this talk, we will present the difficult task of learning their dependency graph, also known as their structure, from data. 

When: Wednesday, 28th of March 2018, 12:00-13:00

Where: Manno, Galleria 1, 2nd floor, room G1-204

Registration: Pizza kebab (or alternative food) and drinks will be offered at the end of the talk. 
If you plan to attend, please register in a timely fashion at the following link so that we will have no shortage of food: 

https://doodle.com/poll/cv5c57q94m76rt2i