The objective of the course is to provide the students the know-how that is required for data analysis in the biomedical field.
Artificial Intelligence has become a fundamental instrument in biomedicine and neurosciences, covering both research translational topics, ranging from the discovery of new numerical biomarkers to support to the diagnosis.
This course aims at providing the students knowledge about the main machine learning methods at the state of the art that are mostly exploited in the field, providing both theoretical bases and implementation tools.
To this end, the course can be considered as a one-week full-immersion tutorial giving main emphasis to the hands-on sessions.
Each session will regard a specific method. The first hour will be devoted to the introduction to the method (setting the theoretical bases) of the duration of about 1 hour, followed by a hand-on session where a specific problem will be tackled and solved. In particular, the hands-on session will start with the illustration and discussion of a piece of code followed by some exercises amining at solving the considered problem.
The lab. sessions will be in Python as this is by far the most exploited tool for this kind of applications in any field.
Papers on the topics faced in the course will be made available in advance as well as instructions regarding the software tools.
The scheduling is as follows
-- LAB 1: Introduction to Python
-- LAB 2: Linear regression (univariate and multivariate)
-- LAB 3: Partial least squares models (PLS)
-- LAB 4: Random Forest
-- LAB 5: SVM (Support Vector Machines)
The exam will consist in a discussion about the topics of the course.
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