Computer Vision
The educational objectives are to provide both theoretical and practical
foundations in computer vision, contributing to the specific
educational objectives of this MSc program. Preliminarily, the course
course will provides the foundations in image acquisition, compression
and processing in space and frequency domains, as well some background
on computer vision. Next, the course will first discuss AI-based
discriminative methods for for computer vision, including applications
to problems like image classification, segmentation and restoration,
object detection, etc. as well as AI-based methods for image and video
synthesis and compression. The course will include both theoretical
lectures in the classroom a well as practical sessions in the lab.
Network Science & Graph Analytics
The objective of this course is to provide students with a comprehensive understanding of the fundamental principles and practical techniques of network science, enabling them to model, analyze, and interpret complex, interconnected systems that arise in real-world applications. The course introduces the core concepts of graph theory and network analysis, including network models, structural properties, connectivity, centrality, robustness, community structure, and spreading phenomena. Students will also develop an understanding of advanced network representations, including weighted and heterogeneous graphs and hypergraphs, and learn how structural properties relate to network evolution and emerging collective behavior. The course further introduces modern graph representation learning techniques, including graph neural networks, with applications to tasks such as node classification and link prediction. Emphasis is also placed on analyzing large-scale graph data and on hands-on experience with computational tools and methods for extracting meaningful insights from complex network structures.
Mathematics for AI & HPC
The course provides the mathematical
tools which serve as a basis for other courses. These tools include
linear algebra concepts, complex numbers, probability and statistics,
numerical analysis and multivariate functions as well as continuous
optimisation algorithms.
Machine & Deep Learning
Language Technologies
This course has two main educational goals. The first is to provide
students with the background knowledge necessary to manipulate and
process natural language data with computational methods. The module
familiarizes the students with the discipline of Computational
Linguistics, its main study objects and problems, as well as the
methodologies for collecting, representing, and process language data
and resources. This goal is instrumental to the second one, of learning
the machine learning techniques that are used to process natural
language, from the representation of language as algebraic structures
and tensors, to a series of neural architectures based on the attention
mechanism used to train general-purpose language models for
classification and generation tasks.