The curriculum in Data Science and Artificial Intelligence for Industry and Cyber-Physical Systems trains graduates who are experts in the application of data science and artificial intelligence methods to industrial problems, particularly in the broad class of cyber-physical systems (e.g. IoT, automation). They will acquire statistical-modeling and optimization skills, machine learning and artificial intelligence skills also applied to control problems, and computational skills in intensive computing and advanced programming.
| 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 | |
| Reinforcement Learning | 6 | |
| One course from Core Group C | 6 | |
| Modelling and Control of Cyber-Physical Systems I | 6 | |
| #colspan# | ||
| II year (60 ECTS) | ||
| Modelling and Control of Cyber-Physical Systems II | 6 | |
| One course from Core Group D | 6 | |
| One course from Core Group E | 6 | |
| One course from Core Group F | 6 | |
| Elective course | 6 | |
| 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 |
| Optimization for Artificial Intelligence | 6 |
| Mathematical Optimization | 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 |
|---|---|
| Ethics and Law of Data and Artificial Intelligence | 6 |
| One elective course | 6 |
It is recommended to include only Numerical Analysis as elective course in the first year, in case a course in numerical analysis was not taken during the undergraduate degree. Otherwise, take Ethics and Law of Data and Artificial Intelligence, which is compulsory, and take the elective course during the second year.
| Core Group D Courses | ECTS |
|---|---|
| Mathematical Optimization | 6 |
| Optimization for Artificial Intelligence | 6 |
| One complementary course from Complementary group | 6 |
If in the first year you took the course in Introduction to Machine Learning, here you have to take one course between Mathematical Optimization or Optimization for AI.
| Core Group E Courses | ECTS |
|---|---|
| Data Management (*) | 6 |
| One complementary course from Complementary group | 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 F Courses | ECTS |
|---|---|
| Ethics and Law of Data and Artificial Intelligence | 6 |
| One elective course | 6 |
If in the first year you took an elective course, here you have to take Ethics and Law of Data and Artificial Intelligence, which is compulsory.
You can add complementary courses from the following groups:
| Complementary Group Courses | ECTS |
|---|---|
| Artificial Intelligence for Cyber-Physical Systems | 6 |
| Simulation Intelligence and Learning for Autonomous Systems | 6 |
You can add in the study plan elective courses from the following group:
| Elective Courses | ECTS |
|---|---|
| All the courses in previous tables | |
| Numerical Analysis | 6 |
| Optimization for Artificial Intelligence | 6 |
| Stochastic Modelling and Simulation | 6 |
| Information Theory | 6 |
| Unsupervised Learning | 6 |
| Computer Vision and Pattern Recognition | 6 |
| Advanced Deep Learning and Kernel Methods | 6 |
| Advanced Topics in Machine Learning | 6 |
| Natural Language Processing | 6 |
| Symbolic and Neuro-Symbolic Artificial Intelligence | 6 |
| Explainable and Reliable Artificial Intelligence | 6 |
| Introduction to Artificial Intelligence | 6 |
| Mathematical Optimisation | 6 |
| Bayesian Statistics | 6 |
| Advanced Probability | 6 |
| Advanced Data Management | 6 |
| Software Development Methods | 6 |
| Machine Learning Operations | 6 |
| Advanced High Performance Computing | 6 |
| GPU and Parallel Programming | 6 |
| Information Retrieval and Data Visualisation | 6 |
| Advanced Statistical Methods | 6 |
| High Performance Computing and Data Infrastructures | 6 |
| Other courses (****) |
(***) Any other course from the University consistent with the study plan