Artificial Intelligence in Quantitative Biology
A.Y. 2026/2027
Learning objectives
Undefined
Expected learning outcomes
Undefined
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Course syllabus
Introduction to the concept of Artificial Intelligence.
Supervised Machine Learning.
Explainable and Interpretable Machine Learning.
Applications in Bioinformatics (Journal Club).
Unsupervised Machine Learning: Clustering Algorithms.
Cluster Explainability.
Applications in Bioinformatics (Journal Club).
Generative Machine Learning.
Applications in Bioinformatics (Journal Club)
Supervised Machine Learning.
Explainable and Interpretable Machine Learning.
Applications in Bioinformatics (Journal Club).
Unsupervised Machine Learning: Clustering Algorithms.
Cluster Explainability.
Applications in Bioinformatics (Journal Club).
Generative Machine Learning.
Applications in Bioinformatics (Journal Club)
Prerequisites for admission
There are no specific prerequisites other than those required to access the Master
Teaching methods
Classes will be in person.
Some of these will be held in a Journal Club format: these classes will involve students in analyzing state-of-the-art articles in computational biology.
Some of these will be held in a Journal Club format: these classes will involve students in analyzing state-of-the-art articles in computational biology.
Teaching Resources
Powerpoint slides used during the lectures.
Journal papers used in the Journal Club.
Journal papers used in the Journal Club.
Assessment methods and Criteria
The evaluation will be based on a scale of 30.
Students will develop a project that they can discuss through an oral exam. This exam will assess their knowledge of the methodologies used to develop the project and alternative methodologies explained in class. The evaluation will also assess the student's ability to communicate and explain the choices made and the results obtained.
Students will develop a project that they can discuss through an oral exam. This exam will assess their knowledge of the methodologies used to develop the project and alternative methodologies explained in class. The evaluation will also assess the student's ability to communicate and explain the choices made and the results obtained.
FIS/07 - APPLIED PHYSICS - University credits: 2
INF/01 - INFORMATICS - University credits: 4
INF/01 - INFORMATICS - University credits: 4
Lessons: 48 hours
Professor:
Casiraghi Elena
Professor(s)