Section outline

◄Introduction
►Course Grading and Exams

  • The module will introduce the fundamentals of AI and machine learning, formalizing the main learning methods and providing knowledge on baseline ML methodologies including regression, neural networks, probabilistic/Bayesian models, causality, complemented with statistical methods for risk estimation and censored data.

      Date Topic References Additional material
    2

    19/02/2026 (14-16)

    Fundamentals of Probability and Statistics for AI

    basic concepts of probability and statistics; random variables and probability distributions; hypothesis testing and statistical inference

    		 [SD] Appendix C1-C3 (refresher)  
    L1

    20/02/2026 (16-18)

    Room M-LAB

    Lab Tutorial 1: Hypothesis testing

    NOTE: Recovery lecture outside of standard course calendar

    		   Other notebooks
    3 24/02/2026 (14-16)

    Machine Learning: fundamentals I

    basic concepts of ML, learning paradigms and fundamental ML tasks, data types and their roles in ML, generalization, bias/variance tradeoff

    [SD] Ch. 1, Sect 2.1

    [AI4H] Pg. 1-16, Pg.  68-74, pg. 81-87

    		  
    L2 25/02/2026 (14-16)

    Lab Tutorial 2: ML with scikit-learn & Model selection with scikit-learn

    		    
    4 26/02/2026 (14-16)

         

    Machine Learning: fundamentals II

    Regularization and model selection

    Machine Learning: Linear Models I

    Linear regression, regularized linear regression (ridge, LASSO, ElasticNet)

    		  [SD] Sect 2.2, sect 6.1.0-6.1.1  More details on the linear regression can be found on this addendum to SD book, in section 8.1 
    5

    27/02/2026 (16-18)

    Room M-LAB

    Machine Learning: Linear Models II

    least square solutions, logistic regression, binary classification, gradient descent training.

    NOTE: Recovery lecture outside of standard course calendar

    		   More details on the logistic regression can be found on this addendum to SD book, in section 9.1
      03/03/2026 (14-16) LECTURE CANCELLED (recovered on 27/02/2026)     
    L3

    04/03/2026 (14-16)

    Lab Tutorial 3: Linear Regression

    		    
    L4

    05/03/2026 (14-16)

    Lab Tutorial 4: Logistic Regression

    		    
    6 10/03/2026 (14-16)

    Artificial Neural Networks I

    introduction to artificial NNs; biological neuron; artificial neuron; multi-layer perceptron; activation functions, input normalization

    		 [SD] Chapter 3 (with the exclusion of Sect. 3.2)  
    L5 11/03/2026 (14-16) Assignment    
    7 12/03/2026 (14-16)

    Artificial Neural Networks II

    output layers; training artificial NNs; backpropagation

    [SD] Sections 6.1 & 6.2: gradient descent

    [SD] Sections 7-1 & 7.2: computing gradients

    		 [SD] Section 7.4: backpropagation (this is additional and in depth material for those that want to know more about how backprop works)
    8 17/03/2026 (14-16)

    Risk stratification

    scoring models; risk factors; assessment of risk predictors; censoring

    		 Slides should be sufficient for this lecture. If you need some additional sources of information here is an highlevel introduction to risk stratification.

    [1] Time-variant logistic regression for risk scoring

    [2] Explainable risk scoring with random forests, XGboost, SVM, NNs

    Software:

    Here is a quite handy library in R for calibration plots
    9 18/03/2026 (14-16)

    Survival analysis

    survival analysis framework; Kaplan-Meier; Cox regression; neural networks for survival analysis; survival trees

    		 [AI4H] Pg. 154-159, pg. 162-168

    [3] Entry-level survey on survival analysis

    [4] Whole textbook on survival analysis

    [5] Paper on survival trees

    Software:

    The Scikit survival analsysis package (datasets, kaplan-meier, cox, survival trees and forests, gradient boosting survival, hypothesis testing)
    L6 19/03/2026 (14-16)

    Lab Tutorial 6: Survival analysis

    		    
             
             
             

◄Introduction
►Course Grading and Exams