Neural Networks and Deep Learning

IMPORTANT NOTICE:
This course will start on January 9, 2024, 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 attend the lectures and receive notifications about any change on course lectures, please register in this file by December 30th.

This course includes three modules of 3 CFUs each: the first module focuses on the theoretical foundations of neural networks and deep learning; the second module covers more advanced topics and recent research trends; the third module covers practical and implementation issues.

Course Program

Module 1: Neural Networks and Deep Learning: Theoretical Foundations
Module 2: Neural Networks and Deep Learning: Advanced Topics
Module 3: Neural Networks and Deep Learning: Implementation Issues
Lecture schedule

Part I: Theoretical Foundations

Introduction to neural computing
Hopfield networks
Unsupervised learning: PCA
Unsupervised learning: Kohonen networks
Culstering algorithms
Reinforcement learning
Supervised learning - part 1
Supervised learning - part 2
Radial Basis Function Networks
Towards Deep Neural Networks (DNNs)
Autoencoders
Convolutional Neural Networks
DNNs for object classification
DNNs for object detection
DNNs for image segmentation
Recurrent Neural Networks
Word embeddings and attention mechanism
Sample applications and possible projects

Part II: Advanced Topics

Deep Reinforcement Learning
Model compression
Generative Adversarial Networks (GANs)
Semi-supervised learning
Contrastive learning
Towards trustworthy AI
Diffusion models (Dall-E)
Adversarial attacks and defenses
Certifiable adversarial robustness
Real-world adversarial examples
Anomaly detection and out-of-distribution generalization
Explainable AI
Transformers for language processing
Transformers for computer vision
Neural networks for real-time tracking

Part III: Implementation Issues

Implementing neural networks in C
Implementing Reinforcement Learning in C
Implementing Backpropagation in C
Frameworks for deep learning
PyTorch and TensorFlow
Functional components in autonomous driving
The Apollo framework for Autonomous Driving
Simulation environments for neural control
Visual tracking with drones
DNN optimization for embedded platforms
Accelerating deep networks on GPGPUs
GPU-based real-time neural vision
Implementing DNNs on GPUs: Advanced Topics
Accelerating deep networks on FPGA
Design and optimization of DNNs accelerators on FPGA