This course provides a comprehensive introduction to the fundamentals of Artificial Intelligence for biomedical and clinical applications. It is designed for students in Bioengineering and covers core machine learning and deep learning methods, along with practical guidance on building experimental pipelines that ensure robustness and reproducibility. Emphasis is placed on both theoretical foundations and hands-on practice, enabling students to apply data-driven approaches to real-world medical problems. The course integrates algorithmic principles with clinical relevance, fostering critical thinking about the opportunities, limitations, and ethical implications of AI in healthcare. Each class combines theoretical lectures with practical sessions to consolidate learning through direct experimentation.
Il corso introdurrà i concetti ei principi fondamentali dell'apprendimento automatico e dell'intelligenza artificiale applicata alla medicina.
Recommended background knowledge
Probability, linear algebra, and statistics.
Calculus
Foundations in programming (preferably Python).
Prior exposure to biomedical signals or systems.
Part 1 – Introduction to AI and Learning Paradigms
Introduction to the course structure, objectives, and evaluation
Fundamentals of Artificial Intelligence and its role in medicine
Overview of data types and challenges in biomedical datasets
Introduction to Python and Google Colab environment
Supervised vs. Unsupervised learning: concepts and comparisons
Introduction to bias, overfitting, and generalization
Part 2 – Machine Learning Methods for Prediction
Core supervised learning techniques (e.g., decision trees, SVM, k-NN)
Evaluation metrics: accuracy, ROC curves, confusion matrix
Introduction to cross-validation and hyperparameter tuning
Part 3 – Biomarker Identification and Feature Selection
Feature selection techniques: filter, wrapper, and embedded methods
Regularization and dimensionality reduction
Biomarker discovery from omics and high-dimensional data
Part 4 – Deep Learning Methods for Prediction
Introduction to deep learning and neural networks
Discussion on different architectures
Part 5 – Applications of AI in Medicine
Case studies across radiology, genomics, and clinical decision support
Ethical and regulatory challenges in clinical AI deployment
Ricevimento: On demand, upon explicit request by email
ANNALISA BARLA (President)
MARGHERITA SQUILLARIO
ALESSANDRO VERRI (President Substitute)
Classes usually start on the last week of September. The schedule of all courses can be found on EasyAcademy.
The final exam consists of two components:
Quiz: A series of multiple-choice and short-answer questions assessing understanding of theoretical concepts. This quiz can be taken either in a single session or progressively throughout the course period, depending on student preference and class scheduling.
Written Test: A practical, open-ended exam where students are required to design and critically discuss machine learning pipelines applied to biomedical problems. Emphasis is placed on the ability to justify methodological choices, address issues of bias and generalization, and ensure interpretability and reproducibility.
The evaluation process is composed of three components:
Test (Entry Quiz) A preliminary quiz on theoretical and practical course content. The test can be taken progressively during the teaching period or in a single session. Passing the test is mandatory to access the written exam.
Written Exam The main assessment consists of a written test where students are required to design and justify machine learning pipelines in response to applied biomedical problems. The written exam grade constitutes the base of the final grade.
Optional Project Students who achieve a grade of 28/30 or higher in the written exam and wish to improve their final grade may complete an individual project. The project involves solving a data analysis task (coding required) and can add or subtract up to 3 points from the written exam grade. The maximum final grade, including the project, is 30 e lode.
