Training and Research
PhD Programme Courses/classes - 2024/2025
Advanced techniques for acquisition of biomedical images
Credits: 1
Language: Ingelese
Teacher: Pietro Bontempi, Federico Boschi
Algorithmic motion planning in robotics
Credits: 1
Language: Italian
Teacher: Paolo Fiorini
Brain Computer Interfaces
Credits: 1,5
Language: Inglese
Teacher: Silvia Francesca Storti
Data visualization
Credits: 1
Language: Italian
Teacher: Andrea Giachetti
Explainable AI models: state of the art, promises and challenges
Credits: 2,5
Language: Italian
Teacher: Gloria Menegaz
Foundation of Robotics Autonomy
Credits: 1
Language: Italian
Teacher: Paolo Fiorini
Generative AI
Credits: 1,5
Language: English
Teacher: Francesco Setti
Modellazione e analisi 3D
Credits: 1
Language: Italian
Teacher: Andrea Giachetti
Modellazione e verifica di sistemi digitali
Credits: 1,5
Language: Italian
Teacher: Franco Fummi, Nicola Bombieri, Graziano Pravadelli
Nanomaterials: synthesis, characterization and applications
Credits: 1
Language: Italian
Teacher: Francesco Enrichi
Soft robotics: from nature to engineering
Credits: 1,5
Language: Italian
Teacher: Francesco Visentin
Techniques and algorithms for biomechanics of movement
Credits: 2,5
Language: Italian
Teacher: Roberto Di Marco
Theranostics: from materials to devices
Credits: 1
Language: Italian
Teacher: Nicola Daldosso
Generative AI (2024/2025)
Teacher
Referent
Credits
1.5
Language
English
Class attendance
Free Choice
Location
VERONA
Learning objectives
In this course, we will introduce the main aspects of generative AI related to the generation of visual content and its connection with semantics and text (text-to-image). We will present basics of generative AI as well as the recent advancements, discussing challenges and promising research lines.
At the end of this course, the student will be able to understand potential and risks related to generative AI, and develop his/her own applications using public tools and pretrained models.
Prerequisites and basic notions
Machine Learning, Deep Learning, Computer Vision, Python programming
Program
- Introduction to genertive AI: definition, main applications, data generation, probabilistic models, generative neural networks.
- Image and video generation: Autoencoders, Generative Adversarial Networks (GANs)
- Text generation: word embeddings, recurrent neural networks, transformer models
- Multimodal generation: diffusion models, text-to-image
- Applications of generative AI
- Tools and resources for generative AI
When and where
Frontal lessons and lab sessions
Learning assessment procedures
Individual project related to the PhD research topic.
Assessment
Class attendance and participation to discussion; project discussion
Criteria for the composition of the final grade
Pass/no pass
PhD school courses/classes - 2024/2025
PhD School training offer to be defined
Faculty
Marziliano Pina
PhD students
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Guidelines for PhD students
Below you will find the files that contain the Guidelines for PhD students and rules for the acquisition of ECTS credits (in Italian: "CFU") for the Academic Year 2024/2025.