Section outline

◄Introduction (2h)
►Learning in probabilistic models

  • The module introduces probabilistic and causal models.  We will refresh useful knowledge from probability and statistics, and introduce fundamental concepts for working with probabilistic models, including conditional independence, d-separation, and causality. We will discuss the graphical models formalism to represent probabilistic relationships in directed/undirected models. 

      Date Topic References
    2 19/02/2026
    (11-13)

    Probability and statistics refresher

    basic concepts of probability and statistics; random variables and probability distributions; Bayes rule, marginalization, family of distributions and their properties; inference in probabilistic models     		  
    3 24/02/2026
    (11-13)

    Graphical models: representation
    Bayesian networks; representing joint distributions; conditional independence;

    Lecture by Riccardo Massidda

    		  
    4 25/02/2026
    (16-18)

    Graphical models: Markov properties
    d-separation; Markov properties; faithfulness; Markov models

    Lecture by Riccardo Massidda

    		  
    5 26/02/2026
    (11-13)

    Graphical Causal Models 

    causation and correlation; causal Bayesian networks; structural causal models; causal Inference

    Lecture by Riccardo Massidda

    		  

     

◄Introduction (2h)
►Learning in probabilistic models