- Knowledge of the main characteristics of artificial intelligence: historical overview and modern definition - Knowledge of how to formalize a learning problem from the mathematical foundations of the optimization objectives to the composition of a deep architecture - Knowledge of the main characteristics of modern deep learning techniques with practical engineering tricks for end-to-end training and fine-tuning the networks - Basic knowledge about how to deal with research questions, how to organize a project and prepare a scientific conference report

- Linear Algebra - Probability theory concepts - Python: Basic elements

Module 1 - Introduction (~4.5h Theory, ~1.5h Laboratory): - Artificial Intelligence: historical definition, brief overview, modern definition and current role of machine and deep learning - Example of a learning problem: linear regression - Introduction to Colab Module 2 - Neural networks basics (~6h Theory, ~7.5h Laboratory): - Perceptron and Artificial Neural Networks - Computational graph of a neural network - Neural Networks basic algorithms: Backpropagation - Gradient Descent based optimization methods and variations - Training a neural network: basics and practice - Introduction to PyTorch: tensor data structures, datasets, data loader, data augmentation Module 3 - Convolutional Neural Networks (~12h Theory, ~6h Laboratory): - Convolutional layers and Convolutional Neural Networks - Normalization and pooling operations in neural networks - Data pre-processing, Weights initialization, Regularization - Transfer Learning - Notable architectures for image classification - Multi-Task and Structured Output Learning (Semantic Segmentation and Detection) Module 4 - Neural Networks for Sequential Data (~6h Theory, ~4.5h Laboratory): - Recurrent Neural Networks - Attention mechanisms - Transformer architecture - Notable Transformer architectures for language and vision Module 5 - Generative Models (~6h Theory): - Unsupervised learning - Autoregressive models - Autoencoders and Variational Autoencoders - Generative adversarial networks - Diffusion models Module 6 - Applications and advanced topics (~6h Theory): - Federated learning - Domain adaptation - Applications with video and temporal data - Applications with 3D data - Exploration of some recent breakthroughs and applications Besides the lectures, the course will include selected topic-dedicated reading group sessions where the students will actively participate by presenting and discussing recent research papers.

- 40 hours of lectures (theory section) - 20 hours of 'laboratory' exercises (practical section)

Slides + State of the art deep learning top conference and journal papers. Optional: specific pointers to chapters of the following books will be indicated during the lectures. - Artificial Intelligence: A Modern Approach S. Russell, P.Norvig - Deep Learning by I. Goodfellow, Y. Bengio, A. Courville. - Dive into Deep Learning by A. Zhang, Z. C. Lipton, M. Li, A. J. Smola, et. al. - Pattern Recognition and Machine Learning, Christopher M. Bishop - Foundations of Computer Vision, Torralba, Isola and Freeman

Video lectures (current year);

Exam: Compulsory oral exam; Group project;

- (50%) Project Work. It consists in reproducing and extending a current state-of-the-art deep learning method. The students can choose among a set of available topics (with constraints on the maximum number of students per topic) and are asked to re-implement an existing approach, run experiments, and test some suggested extensions. The students will work in groups and submit at least 7 days before the oral exam a PDF report of 8 pages in IEEE paper format. This part of the exam aims at evaluating the students' ability in coding and experimentally validating a deep learning model as well as reporting and discussing the obtained results. The evaluation process includes a check on the code and a review of the submitted pdf report. Extra points (for honors, "lode") are assigned to original, well-justified, and experimentally validated method extensions proposed by the students. - (50%) Oral Presentation and Theory questions. This part of the exam aims at verifying the equal contribution of all the authors to the project work. The students are asked to present the work done. The content and timing of the presentation will be evaluated as well as the answers to individual questions. The students are also asked about more general theory topics presented in class.

In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.