Section outline
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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 19/02/2025 (11-13)
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 20/02/2025 (16-18) Lab Tutorial 1: Hypothesis testing Other notebooks on P&S 3 25/02/2025 (16-18) 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 26/02/2025 (11-13) 4 27/02/2025 (16-18) 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 04/03/2025 (16-18)
Machine Learning: Linear Models II
least square solutions, logistic regression, binary classification, gradient descent training.
More details on the logistic regression can be found on this addendum to SD book, in section 9.1 L3 05/03/2025 (11-13)
L4 06/03/2025 (16-18)
6 11/03/2025 (16-18)
Artificial Neural Networks I
introduction to artificial NNs; biological neuron; artificial neuron; multi-layer perceptron
[SD] Chapter 3 (with the exclusion of Sect. 3.2) 7 12/03/2025 (11-13)
Artificial Neural Networks II
activation functions, input normalization; 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) L5 13/03/2025 (16-18)
8 18/03/2025 (16-18)
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 19/03/2025 (11-13)
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 20/03/2025 (16-18)
10 25/03/2025 (16-18)
Bayesian Networks in Healthcare I
graphical formalism; random variables and conditional independence; factorized distributions
[AI4H] Pg. 57-62 11 26/03/2025 (11-13)
Bayesian Networks in Healthcare II
relevant graphical substructures; learning in Bayesian Networks; Bayesian Networks in healthcare
[AI4H] Pg.113-116
Software:
pgmpy - Python package for causal inference and probabilistic inference with Bayesian Networks
12 27/03/2025 (16-18)
Causality and learning dependences
causal relationships and interventions; measuring treatment effects; randomized control trials; discovering dependence in data; structure learning; applications in healthcare and useful libraries
[AI4H] Pg. 197-204, pg. 207-215, pg. 218-224 [6] Tutorial paper on the use of causality in ML
Software:
PyWhy– A full Python-based ecosystem for causal learning
CausalLearn– Python-based structure learning package
L7 01/04/2025 (16-18)