UL Certified Artificial Intelligence Professional

Gain the skills and knowledge needed to integrate the enormous potential of artificial intelligence (AI) into your product portfolio and value chain.

From autonomous driving to material testing and medical image processes, artificial intelligence (AI) and machine learning (ML) are being integrated into an increasing number of technologies in a broad range of industries and applications. To meet evolving industry and market demands, companies must be able to understand the basic elements of AI and ML.

We cover the foundations of AI and ML in our UL Certified Artificial Intelligence Professional training. In this hands-on learning experience, we cover the skills and knowledge participants need to integrate the enormous potential of AI into their product portfolio and value chain. This practice-oriented training covers ML methods, with a particular focus on artificial neural networks, the basis for deep learning, and features applied AI and ML exercises.

Contents

Upon successful completion of this workshop, participants will be able to:

• Describe terms and concepts commonly used when creating and maintaining artificial intelligence systems.
• Understand underlying techniques, use cases and drawbacks of different artificial intelligence methods.
• Design, train and validate neural networks from scratch using modern frameworks.

Take the UL Certified Artificial Intelligence Professional — Foundations exam, a prerequisite for advanced certifications, such as UL Certified Autonomy Safety Professional — Machine Learning.

Day 1: Basics

• Introduction and definition of terms
• Use cases and shortcomings of AI
• Presentation of technologies used in the workshop, such as TensorFlow and Keras
• Example architectures
• Landscape of AI methods
• Supervised learning
• Unsupervised learning
• Reinforcement learning
• Single-layer neuronal networks (perceptrons)
• Biological motivation
• Biological and artificial neurons
• Learning: optimization and gradient descent
• Classification of multiple classes

Day 2: Overview of terms and tools

• Loss functions
• Performance metrics
• Data partitioning
• Feature extraction and dimensionality reduction
• Overfitting and countermeasures
• Multi-layer neural networks
• Backpropagation
• Deep learning
• Introduction to convolution
• Convolutional neural networks
• Convolutional neural networks for image-based problems

Target audience

• Software engineers, developers, data scientists, project leaders, quality managers and testing personnel who are developing artificial intelligence systems.
• Engineers working with advanced driver assistance systems (ADAS) and autonomous vehicle (AV) verification.

Further information

• UL Solutions issues a certificate to participants who successfully pass the exam.
• For online trainings, all materials are offered digitally.