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
    		 Additional Material
    2

    20/02/2026
    (16-18)

    ROOM E

    Introduction to probabilistic learning & models

    module overview; basic concepts of probability and statistics; random variables and probability distributions; Bayes rule, marginalization, family of distributions and their properties; inference in probabilistic models. 

    RECOVERY LECTURE FOR 18/02/2026

     [BRML] Ch. 1 

     [DL] Appendix C1.1-C3.3

    		  
    3 24/02/2026
    (11-13)

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

    Lecture by Riccardo Massidda

    		 [BRML] Sect. 3.1, 3.2 and 3.3.1 (conditional independence)  
    4 25/02/2026
    (16-18)

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

    Lecture by Riccardo Massidda

    [BRML] Sect. 3.3 (conditional independence, d-separation)

    [BRML] Sect. 4.1, 4.2.0-4.2.2 (Undirected Models and Markov Properties) 
    [BRML] Sect. 4.5 (Expressiveness)

    		  
    5 26/02/2026
    (11-13)

    Graphical Causal Models 

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

    Lecture by Riccardo Massidda

    		    
    6 03/03/2026
    (11-13)

    Structure Learning and Causal Discovery 

    constraint-based methods; score-based methods; parametric assumptions 

    Lecture by Riccardo Massidda

    		    

     

◄Introduction (2h)
►Learning in probabilistic models