Deep Learning & Applied AI @Sapienza

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.

Textbook and reading material

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   Open In Colab Open In Colab
Wed 08 Mar Linear regression, convexity, and gradients slides  
Tue 14 Mar Linear models and Pytorch Datasets   Open In Colab
Wed 15 Mar Overfitting and going nonlinear slides  
Tue 21 Mar Logistic Regression and Optimization   Open In Colab
Wed 22 Mar Stochastic gradient descent slides  
Tue 28 Mar Autograd and Modules   Open In Colab
Wed 29 Mar Multi-layer perceptron and back-propagation slides ; video  
Tue 04 Apr Convolutional Neural Networks   Open In Colab
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 Open In Colab
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   Open In Colab
Wed 03 May Geometric deep learning slides; video Open In Colab
Tue 09 May Self-attention and transformers slides Open In Colab
Wed 10 May Adversarial training slides ; video  
Tue 16 May CycleGAN and Adversarial Attacks   Open In Colab
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 Open In Colab