Course material, 2nd semester a.y. 2022/2023, Dept. of Computer Science
Lecturer: Prof. Emanuele Rodolà
Assistants: Dr. Antonio Norelli, Dr. Luca Moschella, Dr. Marco Fumero, Dr. Irene Cannistraci
When: Tuesdays 13:00–16:00 and Wednesdays 13:00–15:00
Physical classrooms: Aula Magna Edificio C RM111 (Tuesdays) and Aula Alfa RM062 (Wednesdays)
There is no virtual classroom, and the lectures will not be recorded.
Q & A: We will use a Discord server. More details during the first lessons.
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, here you can find some supplementary course notes.
Evaluation proceeds according to the following steps:
We may require an oral exam in doubtful cases or whenever necessary.
The list of projects is available here. Each project must be accompanied with code + a 2 page report using a fixed template, available here. Projects can be made in groups of at most 2 students, but in this case, you must motivate this decision and get our approval beforehand.
Here you can find some example sheets of past written exams:
|Date||Topic||Reading||Code & Data|
|Tue 28 Feb||Introduction||slides|
|Wed 01 Mar||Data, features, and embeddings||slides ; linear algebra recap ; matrix notes|
|Tue 07 Mar||Tensor manipulation and Tensor operations|
|Wed 08 Mar||Linear regression, convexity, and gradients||slides|
|Tue 14 Mar||Linear models and Pytorch Datasets|
|Wed 15 Mar||Overfitting and going nonlinear||slides|
|Tue 21 Mar||Logistic Regression and Optimization|
|Wed 22 Mar||Stochastic gradient descent||slides|
|Tue 28 Mar||Autograd and Modules|
|Wed 29 Mar||Multi-layer perceptron and back-propagation||slides ; video|
|Tue 04 Apr||Convolutional Neural Networks|
|Wed 05 Apr||Convolutional Neural Networks||slides|
|Tue 11 Apr||Easter holidays|
|Wed 12 Apr||Regularization, batchnorm and dropout||slides|
|Tue 18 Apr||Uncertainty, regularization and the deep learning toolset||slides|
|Wed 19 Apr||Deep generative models||slides ; video|
|Tue 25 Apr||Liberation Day|
|Wed 26 Apr||Midterm self-evaluation||sheet ; grades|
|Tue 02 May||Variational Autoencoders|
|Wed 03 May||Geometric deep learning||slides; video|
|Tue 09 May||Self-attention and transformers||slides|
|Wed 10 May||Adversarial training||slides ; video|
|Tue 16 May||CycleGAN and Adversarial Attacks|
|Wed 17 May||Invited lecture: Andrea Santilli - “From symbolic representations to ChatGPT”||slides|
|Tue 23 May||Invited lecture: Michele Mancusi and Giorgio Mariani - “Diffusion-based generative models for audio”||slides|