Master's Degree in Data Science (2026-2027)
For the previous year (2025-2026), refer to this page.
Important Info
| News: register on Google Classroom for course news and updates. | |
| Timetable: Thursdays 09:00–12:00 and Fridays 09:00–11:00, room A5 (Via Ariosto), from 24 September to 18 December 2026 |
News
- Registration on Google Classroom is open.
General overview
The course provides a general overview on neural networks as compositions of differentiable blocks, that are optimized numerically. We describe common building blocks including convolutions, self-attention, batch normalization, etc., with a focus on images, texts, and graphs. The course combines rigorous mathematical descriptions with coding sessions in PyTorch / JAX, as well as an overview of the current AI landscape.
Material
Lab sessions are in blue. Homeworks and projects (mandatory) are in red. Seminars (optional) are in green.
| # | Date | Content | Materials | Optional |
|---|---|---|---|---|
| L0 | 24/09/26 | About the course | – | |
| L1 | 24/09/26 | (Historical) introduction | ||
| L2 | 25/09/26 | Preliminaries |
|
|
| PT01 | Introduction to PyTorch | – | ||
| L3 | 15/10/26 | Linear regression and classification | ||
| L4 | Multilayer perceptrons (MLPs) | |||
| PT02 | Logistic regression in PyTorch | – | ||
| L5 | Convolutional neural networks (CNNs) | |||
| L6 | Building deeper models | |||
| PT04 | Convolutional neural networks from scratch | – |
Book: Alice’s Adventures in a Differentiable Wonderland
The course is complemented by a book which expands on most topics covered during the lectures:
- Buy the book on Amazon (independently published to keep the price low).
- Downloaded the updated full draft (29/08/2025).
For the full book webpage (with arXiv version, errata list, and additional chapters): https://sscardapane.it/alice-book/