Course material, 2nd semester a.y. 2024/2025, Dept. of Computer Science
Lecturer: Prof. Emanuele Rodolà
Assistants: Dr. Daniele Solombrino, Dr. Giorgio Strano
When: Mondays 14:00–16:00 and Tuesdays 13:00–16:00
Where:
Physical classroom: Aula L1 - Castro Laurenziano (RM018-E01PTEL026)
There is no virtual classroom, and the lectures will not be recorded.
Python fundamentals; calculus; linear algebra.
Due to the continuously evolving nature of the topic, there is no fixed textbook as a reference. Specific material in the form of scientific articles and book chapters will be given throughout the lectures.
In addition:
Thanks to past and present students for the kind contributions!
Accessibility 👁️🗨️: Since last year, in an effort to create a more inclusive and accessible learning environment, all slides have been re-designed with readability in mind to support students with specific learning disabilities. We aim to ensure that everyone, regardless of learning differences, has equal access to the educational content provided. Should you need additional accommodations or have suggestions for further improving accessibility, please feel free to reach out.
Exam dates
Each exam session has cutoff dates for submitting the project.
Only projects that are (i) submitted before the cutoff date and (ii) registered on Infostud will be graded.
Evaluation proceeds according to the following steps:
We may require an oral exam in doubtful cases or whenever necessary.
Exam is now project-only, but you can still find some past theoretical questions here
Date | Topic | Reading | Code & Data |
---|---|---|---|
Mon 03 Mar | Introduction | slides | |
Tue 04 Mar | Data, features, and embeddings | slides ; linear algebra recap ; matrix notes | |
Mon 10 Mar | Linear regression, convexity, and gradients | slides | |
Tue 11 Mar | Tensor basics and Tensor operations | ||
Mon 17 Mar | Overfitting and going nonlinear | slides | |
Tue 18 Mar | Linear models and Pytorch Datasets | ||
Mon 24 Mar | Stochastic gradient descent | slides | |
Tue 25 Mar | Logistic Regression and Optimization | ||
Mon 31 Mar | Multi-layer perceptron and back-propagation | slides | |
Tue 01 Apr | Autograd and Modules | ||
Mon 07 Apr | Convolutional neural networks | slides ; video | |
Tue 08 Apr | Convolutional neural networks | ||
Mon 14 Apr | Regularization, batchnorm and dropout | slides | |
Tue 15 Apr | Uncertainty, regularization and the deep learning toolset ; Batchnorm and dropout | ||
Mon 21 Apr | Easter holidays | ||
Tue 22 Apr | Easter holidays | ||
Mon 28 Apr | PCA and VAEs | slides | |
Tue 29 Apr | Variational Autoencoders | ||
Mon 05 May | VQ-VAE | ||
Tue 06 May | Tools of the trade | slides | |
Mon 12 May | Adversarial learning & Geometric deep learning | slides | |
Tue 13 May | Adversarial attacks, CycleGAN (optional), and Geometric deep learning (optional) | ||
Mon 19 May | Diffusion models | notes ; source | |
Tue 20 May | Diffusion models | notes | |
Mon 26 May | Self-attention and transformers | slides | |
Tue 27 May | Seminar, Donato Crisostomi: Model Merging - What, Why, and How |
End