Neural Networks for Data Science Applications

Master's Degree in Data Science (2021-2022)

For the previous year (2020-2021), refer to this page.

Important Info

Material: slides, assignments, and grading will be done via Google Classroom.
Timetable: Tuesday, 10-12 AM, Wednesday, 8-11 AM, see official timetable.
In-person attendance: Via Ariosto 25, Room A4 (Tuesday), Room A2 (Wednesday).
Remote attendance (Zoom): Tuesday, Wednesday

General overview

In the course, we first provide a general set of tools and principles to understand deep networks as compositions of differentiable blocks, that are optimized numerically.

Then, we overview common building blocks including convolutions, self-attention, batch normalization, etc., with a focus on image, audio, and graph domains. Towards the end, we overview the deployment of these models (adversarial robustness, interpretability, Dockerization), and some selected state-of-the-art research topics (continual learning, self-supervised learning, …).

The course combines rigorous mathematical descriptions with many coding sessions in TensorFlow.

Slides and notebooks

  Date Content Material
1 21/09/2021 About the course Slides
2 21-22/09/2021 Preliminaries Slides (updated)
Video (part 1)
Video (part 2)
Chapter 2 of the book


  • 1 homework (5 point), 1 final project (10 points), oral examination (15 points).
  • The homework can be recovered in the final project if not done during the course.
  • Lode is given only to students with a project and oral examination highly above average.
  • Optional exercises and reading materials are provided during the course.

Reading material

The main reference book for the course is Dive into Deep Learning. Each set of slides will mention the corresponding sections in the book.