Master's Degree in Data Science (2023-2024)
|Material: slides, assignments, and grading will be done via Google Classroom.|
|Timetable: Wednesday 5-7 PM (Aule A5 + streaming in A6, Via Ariosto), Friday 8-11 (Aule A5 + streaming in A6, Via Ariosto).|
- Classes will start on September 27th (see the faculty calendar).
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 image, audio, and graph domains. The course combines rigorous mathematical descriptions with many coding sessions in TensorFlow.
Lab sessions (mandatory) implemented in TensorFlow are in blue. Homeworks and projects (mandatory) are in red. Seminars (optional) are in green.
|L0||TBD||About the course||[Slides]|