The curriculum in Data Science and Artificial Intelligence for Health and Life Sciences trains graduates who are experts in the construction and application of data science and artificial intelligence methods to medical and biological problems, with particular reference to genomics, neuroscience, epidemiology, and biostatistics. They will acquire statistical-modeling skills, machine learning and artificial intelligence skills, computational skills in intensive computing and advanced programming, and domain knowledge in life sciences and epidemiology.
| Courses | ECTS | |
|---|---|---|
| I year (60 ECTS) | ||
| I semester | ||
| Statistical Methods | 9 | |
| High Performance and Cloud Computing | 9 | |
| One course from Core Group A (+) | 6 | |
| One course from Core Group B | 6 | |
| #colspan# | ||
| II semester | ||
| Probabilistic Machine Learning (++) | 6 | |
| Deep Learning (++) | 6 | |
| Statistical Learning in Epidemiology | 6 | |
| One course from Core Group C | 6 | |
| One course from Core Group D | 6 | |
| #colspan# | ||
| II year (60 ECTS) | ||
| Ethics and Law of Data and Artificial Intelligence | 6 | |
| Computational Genomics | 6 | |
| One course from Core Group E | 6 | |
| Elective courses | 12 | |
| Intership | 12 | |
| Thesis | 18 | |
| Core Group A courses | ECTS |
|---|---|
| Advanced programming (*) | 6 |
| Machine Learning Operations | 6 |
The course in Advanced Programming can be chosen only by students who does not have a solid background in programming (in C, C++ and Python), and who have not taken an advanced programming course during the bachelor. Please contact the program coordinator in case of doubts.
| Core Group B Courses | ECTS |
|---|---|
| Introduction to Machine Learning (*) | 6 |
| Unsupervised Learning | 6 |
The course in Introduction to Machine Learning can be chosen only by students who have not taken an introductory course of machine learning during the bachelor or in other venues. Please contact the program coordinator in case of doubts.
| Core Group C Courses | ECTS |
|---|---|
| Algorithmic Design (*) | 6 |
| Algorithmic Data Mining | 6 |
The course in Algorithmic Design can be chosen only by students who have not taken an introductory course of algorithms and data structures during the bachelor or in other venues. Please contact the program coordinator in case of doubts.
| Core Group D Courses | ECTS |
|---|---|
| Data Management (*) | 6 |
| Stochastic Modelling and Simulation | 6 |
| Advanced Statistical Methods | 6 |
The course in Data Management can be chosen only by students who have not taken an course of databases or data management during the bachelor or in other venues. Please contact the program coordinator in case of doubts.
| Core Group E Courses | ECTS |
|---|---|
| Unsupervised Learning | 6 |
| One complementary course from Complementary group | 6 |
The course in Unsupervised Learning must be taken by students who have not taken it during the first year.
You can add complementary courses from the following group:
| Complementary Group Courses | ECTS |
|---|---|
| Stochastic Modelling and Simulation | 6 |
| Computational Neuroscience | 6 |
| Advanced Statistical Methods | 6 |
You can add in the study plan elective courses from the following group:
| Elective Courses | ECTS |
|---|---|
| All the courses in previous tables | |
| Molecular Biology | 6 |
| Information Theory | 6 |
| Management of Health Data | 6 |
| Molecular Simulation | 6 |
| Advanced Deep Learning and Kernel Methods | 6 |
| Computer Vision and Pattern Recognition | 6 |
| Natural Language Processing | 6 |
| Information Retrieval and Data Visualisation | 6 |
| Mathematical Optimisation | 6 |
| Bayesian Statistics | 6 |
| Advanced Data Management | 6 |
| Software Development Methods | 6 |
| Machine Learning Operations | 6 |
| Bioinformatics | 6 |
| Multi-Agent Systems | 6 |
| Simulation Intelligence and Learning for Autonomous Systems | 6 |
| Symbolic and Neuro-Symbolic Artificial Intelligence | 6 |
| Explainable and Reliable Artificial Intelligence | 6 |
| Advanced High Performance Computing | 6 |
| High Performance Computing and Data Infrastructures | 6 |
| Other Courses (****) |
(***) Any other course from the University consistent with the study plan