Course material, 2nd semester a.y. 2023/2024, Dept. of Computer Science
- 14/09/2024: The grades for the written exam of September 10th are available here.
- 04/09/2024: The written exam of September 10th will be held in Aula 2L from 14:00 to 16:00.
- 16/07/2024: The grades for the written exam of July 10th are available here.
- 20/06/2024: The written exam of July 10th will be held in Aula 2L from 9:00 to 11:00.
- 18/06/2024: The latest possible date to deliver the project for the July session is Friday 19 July.
- 12/06/2024: The solutions for the written exam of June 5th are available here.
- 10/06/2024: The grades for the written exam of June 5th are available here.
- 24/05/2024: The written exam of June 5th will be held in Aula 2L from 12:00 to 14:00.
- 21/05/2024: The course if finished, see you at the exam!
- 15/05/2024: The project list is published here.
- 06/05/2024: The midterm results are published here.
- 05/05/2024: Due to the Professor and the entire research team attending ICLR 2024 to present several works, the lab session of Tue 07 May will be conducted offline. Reach out to the Professor via email for questions and issues on the notebook.
- 29/04/2024: The midterm sheet is published, scroll down to download it. If you want me to grade your answers, please send your solutions via email by 17:00.
- 16/04/2024: The midterm self-assessment will take place on April 29th.
- 22/03/2024: Mark your calendars! The exam dates ✍ are now published (scroll down to the Grading section).
- 20/03/2024: The lecture of Monday 8 April will be held in Aula RE3, Building A of Viale Regina Elena 291.
- 12/03/2024: Added an explanation for the solution of the final Exercise 3 of Notebook 2 (Tensor operations).
- 05/02/2024: The course website is online. Welcome to DLAI 2023/24! The course will start on Mon 26th February.
Lecturer: Prof. Emanuele Rodolà
Assistants: Dr. Donato Crisostomi, Dr. Adrian Minut, Dr. Daniele Solombrino
When: Mondays 14:00--16:00 and Tuesdays 13:00--16:00
Where:
Physical classroom: Aula L2 - Castro Laurenziano (RM018-E01PTEL026)
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.
Accessibility 👁️🗨️: Starting from this semester, 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
5 June 202410 July 202410 September 2024
Evaluation proceeds according to the following steps:
- A midterm self-evaluation test (optional, does not concur to the final grade)
- A final written exam (mandatory, accounts for 60% of the final grade)
- A project (mandatory, accounts for 40% of the final grade)
- An oral exam (optional, attributes at most 3 points, added to or subtracted from the final grade)
We may require an oral exam in doubtful cases or whenever necessary.
You can find all past written exams in this Google Drive folder
| Date | Topic | Reading | Code & Data |
|---|---|---|---|
| Mon 26 Feb | Introduction | slides | |
| Tue 27 Feb | Data, features, and embeddings | slides ; linear algebra recap ; matrix notes | |
| Mon 04 Mar | Linear regression, convexity, and gradients | slides | |
| Tue 05 Mar | Tensor basics and Tensor operations | ||
| Mon 11 Mar | Overfitting and going nonlinear | slides | |
| Tue 12 Mar | Linear models and Pytorch Datasets | ||
| Mon 18 Mar | Stochastic gradient descent | slides | |
| Tue 19 Mar | Logistic Regression and Optimization | ||
| Mon 25 Mar | Multi-layer perceptron and back-propagation | slides | |
| Tue 26 Mar | Autograd and Modules | ||
| Mon 01 Apr | Easter holidays | ||
| Tue 02 Apr | Easter holidays | ||
| Mon 08 Apr | Convolutional neural networks | slides | |
| Tue 09 Apr | Convolutional neural networks | ||
| Mon 15 Apr | Regularization, batchnorm and dropout | slides | |
| Tue 16 Apr | Uncertainty, regularization and the deep learning toolset | ||
| Mon 22 Apr | PCA and VAEs | slides | |
| Tue 23 Apr | Variational Autoencoders | ||
| Mon 29 Apr | Midterm | sheet | |
| Tue 30 Apr | Lab catch-up | complete all the published notebooks | |
| Mon 06 May | Adversarial learning | slides ; video | |
| Tue 07 May | CycleGAN and Adversarial Attacks | ||
| Mon 13 May | Geometric deep learning | slides ; video | |
| Tue 14 May | Reinforcement Learning tutorial | slides | |
| Mon 20 May | Self-attention and transformers | slides ; Training neural networks effectively | |
| Tue 21 May | Create your own agent 👤 | source code |
End