Intelligent Monitoring and Control Systems

The course introduces artificial intelligence techniques for designing, training, and effectively deploying intelligent systems across a diverse array of industrial applications. It begins with an overview of standards, certifications, and legislation pertaining to industrial monitoring. Subsequently, it delves into design techniques for intelligent systems, encompassing the collection of training data and various learning methods. Additionally, the course explores practical use cases, tools, and implementation environments.

Main topics covered:

- Legislation regarding industrial monitoring: standards and certifications
- Description of intelligent systems for industrial automation, robotics, power distribution grids, automotive, and transport systems
- Current trends, applications, and major challenges in artificial intelligence
- Application of Artificial Intelligence in industrial processes
- Techniques for data augmentation and transfer learning
- Defect analysis of products and production lines, along with anomaly detection
- Predictive maintenance strategies for industrial components
- Methods for making industrial applications explainable
- Optimization of neural networks for edge devices
- Optimization techniques for memory efficiency in edge computing
- Generative modeling for control systems and industrial scenarios
- Techniques for Image Quality Assessment (IQA)
- Detection and segmentation methods
- Self-supervised Learning (SSL) for intelligent systems
- Attention networks and memory in anomaly detection
- Continual learning for control and optimization
- Application of federated learning and graph-based methods in control systems
- Unimodal and multimodal learning, and fusion of information

A detailed list of topics for each lesson is provided and regularly updated on the course website.