6.3.- ARTIFICIAL INTELLIGENCE, MACHINE LEARNING AND COMPUTER VISION

Artificial Intelligence (AI) is a branch of computer science that enables the development of intelligent systems capable of analyzing information, identifying patterns, making predictions, and making decisions based on data. One of its main disciplines is Machine Learning (ML), which enables the development of models that learn from datasets without the need to explicitly program every operating rule. Within this field, Deep Learning (DL) uses artificial neural networks to solve more complex problems. Meanwhile, Computer Vision enables computer systems to process and interpret images and videos using image processing and Artificial Intelligence techniques, supporting applications such as object recognition, image classification, defect detection, and automated inspection.

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The integration of Artificial Intelligence, Machine Learning, and Computer Vision into industrial processes is one of the key technologies driving Industry 4.0 and intelligent automation. The combination of sensors, actuators, data acquisition systems, the Internet of Things (IoT), and Edge Computing technologies enables the development of systems capable of acquiring, processing, and analyzing information from physical processes. These technologies support real-time monitoring, anomaly detection, predictive maintenance, intelligent control, industrial process optimization, and automated decision-making. Furthermore, Edge AI enables Artificial Intelligence models to run directly on devices located close to the data source, reducing dependence on remote servers and facilitating applications that require rapid responses.

Training in Artificial Intelligence, Machine Learning, and Computer Vision is essential for preparing students and professionals to develop technological solutions for engineering, automation, and industrial systems. Artificial Intelligence teaching equipment and laboratories provide hands-on experience throughout the complete development cycle of intelligent applications, from data acquisition, storage, and preparation to the training, evaluation, and deployment of Machine Learning and Deep Learning models. By integrating cameras, sensors, actuators, IoT communication systems, and Edge Computing devices, students can develop applications involving image recognition and classification, data analysis, anomaly detection, and physical process control, connecting the theoretical foundations of Artificial Intelligence with practical applications in real-world environments.

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ARTIFICIAL INTELLIGENCE, MACHINE LEARNING AND COMPUTER VISION EQUIPMENT

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