The Master's degree offers 4 different Curricula. The list of courses, including short descriptions, are listed on this page.
Curricula
A. Methodologies and Applications
- 48 CFU Mandatory courses
- 12 CFU In-depth courses
- 18 CFU Path, choose among:
- 12 CFU Free-choice courses
- 24 CFU Thesis
- 6 CFU Internship
B. AI for Innovation
- 48 CFU Mandatory courses
- 12 CFU In-depth courses
- 18 CFU Path AI and Innovation
- 12 CFU Free-choice courses
- 24 CFU Thesis
- 6 CFU Internship
C. Systems
- 48 CFU Mandatory courses
- 12 CFU In-depth courses
- 18 CFU Path Systems
- 12 CFU Free-choice courses
- 24 CFU Thesis
- 6 CFU Internship
D. Neurocognitive Architectures
- 48 CFU Mandatory courses
- 30 CFU Track Neurocognitive Architectures
- 12 CFU Free-choice courses
- 24 CFU Thesis
- 6 CFU Internship
Courses | Credits (ECTS) |
---|---|
Fundamentals of Artificial Intelligence Artificial Intelligence (AI) is an umbrella term, covering a large and heterogeneous amount of disciplines. This course aims at providing an overview of the foundations of AI and of its main disciplines (e.g. problem solving, knowledge representation and reasoning, planning, uncertain knowledge, learning, perception, ...) in an organic way. Examples and exercises will be provided during the course. |
12 |
Machine Learning The course aims at studying the fundamentals of machine learning, covering supervised and unsupervised learning methods and deep learning approaches. It includes application examples as well as laboratory exercises. |
12 |
Natural Language Understanding Natural Language Understanding is the fundamental component of artificial intelligence systems ( AIS ) that communicate with humans. AIS communicate directly with humans via the conversational interfaces of social robots. AIS may be able to read and comprehend vast amounts of human language data ( speech, text or multimedia ) and makes sense. In the first part of the course we will provide the students the basic knowledge about the natural language structure from the lexicon to the document level, formal models for representing the lexicon, the sentence and the discourse. We will present and provide the machine learning models to learn language structures from language corpora. In the last part of the course we will describe use cases of Natural Language Understanding in AIS. Students will be trained to train simple natural language understanding models for different use cases. |
6 |
Artificial and biological Neural Systems Students will acquire the ability to understand principles of cognitive neuroscience that are relevant for understanding current thinking in AI and Cognitive Computational Neuroscience. They will be able to consider application of neuroscience to AI, and conversely, potential applications of AI to neuroscience, taking into consideration both similarities and differences in the representations of the human and artificial systems. |
6 |
Signal, Image and Video The course provides the basic competences in the field of digital signal processing, with special attention to images and video sequences. Starting from the fundamentals of 1D signal analysis and processing, analog and numerical, in time and frequency domains, we then extend the concepts to the multi-dimensional case of signals in space. Then, we introduce the more important approaches for image filtering and extraction of image descriptors. These concepts are further extended to deal with motion pictures. Finally, the problem of image compression is introduced, focusing on most known techniques for image and video coding, as well as their standard implementations. The approach of the course is rather practical, with the explanation of theoretical concept followed by their translation into algorithmic terms. |
6 |
Law and Ethics in Artificial Intelligence The course objective is to educate professional figures fully aware of the complex impact of AI and robotics on our society. In this respect, the student will be offered basic information on the ethical and legal implication of AI. Specifically, the course will expose the most important motives for a truly human centred AI and to the bioethical principles that could underly its construction. At the same time, the course will present and discuss the main regulatory tools that could be applied to AI at different levels (national regulations, European regulations, international regulations). The students will be interactively involved through the discussion of realistic cases and through the presentation of papers. |
6 |
Paths
Select one specialization area and corresponding 18 credits from one of the following paths:
Activities | Credits (ECTS) |
---|---|
Internship |
6 |
Thesis |
24 |