Training and Research
PhD Programme Courses/classes
This page shows the PhD course's training activities for the academic year 2024/2025. Further activities will be added during the year. Please check regularly for updates!
Modelli dinamici e simulazione di sistemi multibody
Credits: 5
Language: English
Teacher: Iacopo Tamellin
Advanced techniques for acquisition of biomedical images
Credits: 1
Language: Ingelese
Teacher: Pietro Bontempi, Federico Boschi
Theranostics: from materials to devices
Credits: 1
Language: english
Teacher: Nicola Daldosso, Tommaso Del Rosso
Nanomaterials: synthesis, characterization and applications
Credits: 1
Language: English
Teacher: Francesco Enrichi, Tommaso Del Rosso
Brain Computer Interfaces
Credits: 3
Language: Inglese
Teacher: Silvia Francesca Storti
Algorithmic motion planning in robotics
Credits: 1
Language: Italian
Teacher: Paolo Fiorini
Data visualization
Credits: 1
Language: Inglese
Teacher: Andrea Giachetti
Modellazione e analisi 3D
Credits: 1
Language: Inglese
Teacher: Andrea Giachetti
Sistemi Ciber-Fisici nell’Industria 4.0: Modellazione, Reti e Intelligenza
Credits: 3
Language: English
Teacher: Enrico Fraccaroli
Modelli di Intelligenza Artificiale Spiegabile: stato dell'arte, promesse e sfide
Credits: 2.5
Language: Inglese
Teacher: Gloria Menegaz
Foundation of Robotics Autonomy
Credits: 1
Language: Italian
Teacher: Paolo Fiorini
Generative AI
Credits: 1.5
Language: English
Teacher: Francesco Setti
Modeling and Verification of Digital Systems
Credits: 1.5
Language: Italian
Teacher: Franco Fummi, Nicola Bombieri, Graziano Pravadelli
Soft robotics: from nature to engineering
Credits: 1.5
Language: English
Teacher: Francesco Visentin
Techniques and algorithms for biomechanics of movement
Credits: 2.5
Language: English
Teacher: Roberto Di Marco
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
Didactic methods
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
