Neural Networks and Deep Learning

IMPORTANT NOTICE:
This course will start on January 13, 2026, at 9:00.
Lectures will be opened to everybody and will be given online on the following channel.
Please, connect 10 minutes before 9:00 to avoid disturbing the lecture.

Registration

To access the channel of the lectures and receive notifications about any change on course lectures, please register here by December 15th.

Certification of attendance

If you need a certification of attendance, be aware that the number of hours that will be certified are those recorded by Teams.

Course Program and Lectures Schedule

This course includes four modules:

The first module (30 hours) focuses on the theoretical foundations of neural networks, including deep learning and convolutional networks.
The second module (20 hours) focuses on advanced topics, including transformers, deep RL, semi-supervised learning, and generative networks.
The third module (20 hours) focuses on how to make deep networks more trustworthy, interpretable, and secure.
The fourth module (30 hours) covers practical and implementation issues.

Part I: Theoretical Foundations

Introduction to neural computing
Hopfield networks
Unsupervised learning: PCA
Unsupervised learning: Self-Organizing Maps
Clustering algorithms
Reinforcement learning
Supervised learning and Backpropagation
Performance metrics
Radial Basis Function Networks
Towards Deep Neural Networks (DNNs)
Autoencoders
Convolutional Neural Networks (CNNs)
CNNs for object classification
CNNs for object detection
CNNs for image segmentation

Part II: Advanced Topics

Recurrent neural networks
Natural language processing
Word embedding and attention mechanism
Transformers
Deep reinforcement learning
Semi-supervised learning
Special deep learning models
Neural networks for multi-object tracking
Generative networks

Part III: Trustworthy AI

Explainable and Interpretable AI
Anomaly and out-of-distribution detection methods
Domain generalization and domain adaptation
Attention mechanisms in computer vision
Adversarial attacks and defenses
Real-world attacks and defenses
Simulators for autonomous driving
Hardware in the loop simulation
Functional components in autonomous driving
The Autoware framework for autonomous driving

Part IV: Implementation Issues

Implementing neural networks in C
Programming frameworks for deep learning
Modeling DNNs in Tensorflow and PyTorch
GPU programming in CUDA
Accelerating deep networks on GPGPUs
DNN optimization for embedded platforms
The NVIDIA TensorRT framework
Accelerating deep networks on FPGA
The Xilinx Deep Processing Unit

Exam

The exam (for those that need to take it) consists in a project development. The project is one, independently on the number of modules attended, but the number of hours that will be certified are those recorded by Teams. The completion of the exam requires the project discussion and the delivery of the project code, including a report describing the work done. Please, read carefully the project rules in the link below.

Project rules
Projects list (it will be posted later)
Projects availability (it will be posted later)