Neural Networks. Deep Learning

DESCRIPTION

Nowadays, Artificial Intelligence drives scientific and economic growth worldwide. This is largely due to advances in Machine Learning (ML), notably in Deep Neural Networks (DNNs), which are essentially massive ‘learning by experience/examples’ systems. Their applications span and revolutionize almost every human activity:

• Autonomous Systems (cars, drones, vessels),
• Media Content and Art Creation (including fake data creation/detection), Social Media Analytics,
• Medical Imaging and Diagnosis,
• Financial Engineering (forecasting and analytics), Big Data Analytics,
• Broadcasting, Internet and Communications,
• Robotics/Control
• Intelligent Human-Machine Interaction, Anthropocentric (human-centered)Computing,
• Smart Cities/Buildings and Assisted living.
• Scientific Modeling and Analytics.

Several DNN advances and challenges hit the news almost every day, arising discussions on AI ethics, privacy protection and its societal impact.

This CVML Web Module focuses on focuses on Machine Learning and Deep Neural Network theory, their applications in the above-mentioned diverse domains and new challenges ahead. As there is much hype and often little accuracy, when treating DNN topics, a rigorous mathematical treatment of all DNN topic is included in each lecture, focusing on both classification and regression problems.

The cornerstone DNN theories and technologies are presented: a) Artificial Neural Networks, Perceptron; b) Multilayer perceptron, Backpropagation; c) Convolutional Neural Networks (CNNs), both data classification and regression problems; d) Autoencoders; e) Recurrent Neural Networks. Applications follow in several image analysis, computer vision and autonomous system applications, notably: a) Deep learning for object detection, including special topics, e.g., on small object detection;  b) Few-Shot Object Recognition; c) Deep Semantic Image Segmentation. Generative Adversarial Networks are presented that promise to revolutionize the way we create media/arts, while seriously threatening our democracy with fake data creation and spread. Deep Reinforcement Learning is also presented, as it is an essential element in novel Robotics/Control and other decision-making application domains.

Artificial Neuron.

LECTURE LIST

1. Artificial Neural Networks. Perceptron
2. Convolutional Neural Networks
3. Deep Autoencoders
4. Deep Object Detection
5. Deep Reinforcement Learning
6. Deep Semantic Image Segmentation
7. Federated Learning
8. Few Shot Object Recognition
9. Generative Adversarial Networks in Multimedia Creation
10. Multilayer Perceptron. Backpropagation.
11. Recurrent Neural Networks
12. Special topics in Object Detection
13. Attention and Transformer Networks