Study Guide

Objective

You know the basics of intelligent automation; key concepts, applications, objectives, opportunities and threats.
You know and are able to apply the possibilities of data-driven machine and deep learning to enable intelligent automation in different application areas (eg. processes, robotics)
You know and are able to apply various intelligent automation platforms, services and tools.
You know and are able to evaluate the suitability of intelligent automation technologies.
You understand the importance of information security in relation to intelligent automation solutions.
You are able to apply solutions that improve information security in the implementation of intelligent automation.

Content

Fundamentals, concepts, benefits and challenges of intelligent automation, applications and technologies.
Intelligent automation environments and platforms; on-premises, cloud services, hybrid solutions.
Artificial intelligence for use in automation; opportunities for machine learning and in-depth learning, as well as application principles.
Basics of deep learning; neural networks (ANN), deep neural networks (CNN), feedback neural networks (RNN), enhanced learning, GANN networks.
Deep learning and automation; cognitive automation machine vision, natural language processing)
Intelligent automation and the possibilities of quantum machine learning (QML); classical computing vs. quantum computing in automation, quantum-classical hybrids, QaaS quantum computing services in automation.
Intelligent automation security.

Evaluation scale

H-5