Curriculum Foundations of Artificial Intelligence and Machine Learning

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.

CoursesECTS
I year (60 ECTS)
I semester
Statistical Methods 9
High Performance and Cloud Computing9
One course from Core Group A6
One course from Core Group B6
#colspan#
II semester
Probabilistic Machine Learning (+)6
Deep Learning (+)6
Reinforcement Learning6
Ethics and Law of Data and Artificial Intelligence6
One course from Core Group C6
#colspan#
II year (60 ECTS)
One course from Core Group D6
One course from Core Group E6
One course from Complementary Group A 6
Elective courses12
Intership12
Thesis18

(+), (++): Integrated courses (two modules combined in a single course)


Core Group A CoursesECTS
Advanced programming (*)6
Machine Learning Operations6

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 CoursesECTS
Introduction to Machine Learning (*)6
Optimization for Artificial Intelligence6

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 CoursesECTS
Algorithmic Design (*)6
Algorithmic Data Mining6

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 CoursesECTS
Optimization for Artificial Intelligence6
One complementary course from Complementary group B6

The course in Optimization for Artificial Intelligence must be taken by students who have not taken it during the first year.

Core Group E CoursesECTS
Data Management6
One complementary course from Complementary group B6

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 CoursesECTS
Introduction to Artificial Intelligence (*)6
Symbolic and Neuro-Symbolic Artificial Intelligence6
Explainable and Reliable Artificial Intelligence6
Multi-Agent Systems6
Simulation Intelligence and Learning for Autonomous Systems6

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 CoursesECTS
Unsupervised Learning6
Computer Vision and Pattern Recognition6
Advanced Deep Learning and Kernel Methods6
Natural Language Processing6
Advanced Statistical Methods6
Information Retrieval and Data Visualisation6
Advanced Data Management6

You can add in the study plan elective courses from the following group:

Elective CoursesECTS
All the courses of previous tables
Information Theory6
Data Management6
Stochastic Modelling and Simulation 6
Mathematical Optimisation6
Bayesian Statistics 6
Machine Learning Operations6
Advanced Topics in Machine Learning6
Software Development Methods6
Advanced High Performance Computing6
High Performance Computing and Data Infrastructures6
Artificial Intelligence for Cyber-Physical Systems6
Other courses (***)

(***) Any other course from the University consistent with the study plan