Master's Degree in Data Science (2021-2022)
|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|
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
|1||21/09/2021||About the course||Slides|
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.
The main reference book for the course is Dive into Deep Learning. Each set of slides will mention the corresponding sections in the book.