The curriculum in Foundations of Artificial Intelligence and Machine Learning trains graduates to become experts in modern Artificial Intelligence techniques, particularly in Machine Learning techniques. Students will acquire statistical and modelling skills, as well as classic Artificial Intelligence and state-of-the-art machine learning competences, computational skills for intensive computing, and advanced programming skills.
| 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 | |
| Ethics and Law of Data and Artificial Intelligence | 6 | |
| One course from Core Group C | 6 | |
| #colspan# | ||
| II year (60 ECTS) | ||
| One course from Core Group D | 6 | |
| One course from Core Group E | 6 | |
| One course from Complementary Group A | 6 | |
| Elective courses | 12 | |
| Intership | 12 | |
| Thesis | 18 | |
(+), (++): Integrated courses (two modules combined in a single course)
| 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 |
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 |
|---|---|
| Optimization for Artificial Intelligence | 6 |
| One complementary course from Complementary group B | 6 |
The course in Optimization for Artificial Intelligence must be taken by students who have not taken it during the first year.
| Core Group E Courses | ECTS |
|---|---|
| Data Management | 6 |
| One complementary course from Complementary group B | 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.
You can add complementary courses from the following groups:
| Complementary Group A Courses | ECTS |
|---|---|
| Introduction to Artificial Intelligence (*) | 6 |
| Symbolic and Neuro-Symbolic Artificial Intelligence | 6 |
| Explainable and Reliable Artificial Intelligence | 6 |
| Multi-Agent Systems | 6 |
| Simulation Intelligence and Learning for Autonomous Systems | 6 |
The course in Introduction to Artificial Intelligence can be chosen only by students who have not taken a similar course during the bachelor or in other venues. Please contact the program coordinator in case of doubts.
| Complementary Group B Courses | ECTS |
|---|---|
| Unsupervised Learning | 6 |
| Computer Vision and Pattern Recognition | 6 |
| Advanced Deep Learning and Kernel Methods | 6 |
| Natural Language Processing | 6 |
| Advanced Statistical Methods | 6 |
| Information Retrieval and Data Visualisation | 6 |
| Advanced Data Management | 6 |
You can add in the study plan elective courses from the following group:
| Elective Courses | ECTS |
|---|---|
| All the courses of previous tables | |
| Information Theory | 6 |
| Data Management | 6 |
| Stochastic Modelling and Simulation | 6 |
| Mathematical Optimisation | 6 |
| Bayesian Statistics | 6 |
| Machine Learning Operations | 6 |
| Advanced Topics in Machine Learning | 6 |
| Software Development Methods | 6 |
| Advanced High Performance Computing | 6 |
| High Performance Computing and Data Infrastructures | 6 |
| Artificial Intelligence for Cyber-Physical Systems | 6 |
| Other courses (***) |
(***) Any other course from the University consistent with the study plan