Training and Research
PhD Programme Courses/classes
Techniques and algorithms for biomechanics of movement
Credits: 2.5
Language: English
Teacher: Roberto Di Marco
Teranostica: dai materiali ai dispositivi
Credits: 3
Language: english
Teacher: Guilherme C. Concas, Nicola Daldosso
Advanced techniques for acquisition of biomedical images
Credits: 2.5
Language: Inglese
Teacher: Pietro Bontempi, Federico Boschi
Nanomaterials: synthesis, characterization and applications
Credits: 3
Language: English
Teacher: Francesco Enrichi, Guilherme Concas
AI Soft robotics: from nature to engineering
Credits: 1.5
Language: English
Teacher: Francesco Visentin
Dinamica dei sistemi multicorpo: modellistica e simulazione
Credits: 5
Language: Inglese / English.
Teacher: Iacopo Tamellin
3D modeling and analysis
Credits: 1
Language: Inglese
Teacher: Andrea Giachetti
Fixed Points, Feedback, and Adaptive Computation
Credits: 3
Language: Inglese
Teacher: Gloria Menegaz
Generative AI (part I)
Credits: 1.5
Language: English
Teacher: Marco Cristani
Generative AI (part II)
Credits: 1.5
Language: English
Teacher: Francesco Setti
Fixed Points, Feedback, and Adaptive Computation (2025/2026)
Teacher
Referent
Credits
3
Language
Inglese
Class attendance
Free Choice
Learning objectives
The course is designed to bring students up to speed on the mechanics of modern AI models to establish a shared baseline. From there, we will cover the fundamentals of Automatic Differentiation (AD) and rapidly progress to the mechanics of differentiating through fixed points (utilizing techniques like implicit differentiation and phantom gradients). Finally, the course will survey cutting-edge literature, exploring how to bridge the gap between fixed-computation and iterative reasoning , address induction biases in sequence models , and build introspective networks that dynamically adjust to input complexity.
Prerequisites and basic notions
NA
Program
Key Topics Covered:
• Modern AI Architectures: A rapid recap of current state-of-the-art models to get everyone up to speed.
• Advanced Automatic Differentiation: Mechanics of AD and backpropagation through iterative algorithms.
• Fixed-Point Iteration in Deep Learning: Techniques for differentiating through fixed points without unrolling, ensuring a constant memory footprint.
• Adaptive Computation & Introspection: Designing models that dynamically adjust their computational depth and allocate resources based on the difficulty of the task.
• State Tracking and Induction Bias: Analyzing the in-distribution and out-of-distribution capabilities of sequence models.
Proposed Schedule (6 Lectures):
1. Lecture 1: Thursday April 9, 2026 – 11:00
2. Lecture 2: Friday, April 10, 2026 – 11:00
3. Lecture 3: Monday, April 13, 2026 – 11:00
4. Lecture 4: Wednesday, April 15, 2026 – 11:00
5. Lecture 5: Friday, April 17, 2026 –11:00
6. Lecture 6: Monday, April 20, 2026 – 11:00
Note on Course Material: This course dives into the bleeding edge of AI research. Much of the reading material, discussions, and core concepts will be based on papers published within the last 12 months—including highly recent preprints and active conference submissions.
Didactic methods
In presence
Learning assessment procedures
Discussion on the course topics
Assessment
Ability to critically present the course topics
Criteria for the composition of the final grade
The grade will be in the form Pass/Fail. The course corresponds to 3 CFU (1 CFU/4 lesson hour).
