Neural Networks for Data Science Applications

Master's Degree in Data Science (2023-2024)

For the previous year (2022-2023), refer to this page.

Important Info

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).

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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 image, audio, and graph domains. The course combines rigorous mathematical descriptions with many coding sessions in TensorFlow.

More information about the topics, the exam, organization, etc. can be found on the introductory slides.

Material

Lab sessions (mandatory) implemented in TensorFlow are in blue. Homeworks and projects (mandatory) are in red. Seminars (optional) are in green.

  Date Content Material
L0 TBD About the course [Slides]