Artificial Intelligence (AI) is a methodology for using a non-human system to learn from experience and imitate human intelligent behavior. The APMG Foundation Certificate in Artificial Intelligence tests a candidate’s knowledge and understanding of AI terminology and general principles.
This syllabus covers the potential benefits and challenges of Ethical and Sustainable Robust Artificial Intelligence; the basic process of Machine Learning (ML) – Building a Machine Learning Toolkit; the challenges and risks associated with an AI project; and the future of AI and Humans in work.
Artificial Intelligence Certification Training Delivery Methods
Artificial Intelligence Certification Training Information
- Human-centric ethical and sustainable human and artificial intelligence.
- Artificial intelligence and robotics.
- Apply the benefits of AI projects – challenges and risks.
- Machine learning theory and practice – building a machine learning toolbox.
- The management, roles and responsibilities of humans and machines – the future of AI.
Recommended to have basic IT literacy and awareness of business processes.
Certification Exam Information
The APMG Foundation Certificate in Artificial Intelligence is a foundation-level certification focused on core AI concepts, technologies, and applications intended for professionals getting started in AI.
Duration: 60-minute, closed-book exam
Number of Questions: 40 multiple-choice questions
Passing Score: Answer 26 out of 40 questions (achieve 65% or above)
Artificial Intelligence Certification Training Outline
- Recall the general definition of Human and Artificial Intelligence (AI)
- Describe the concept of intelligent agents.
- Describe a modern approach to Human logical levels of thinking using Robert Dilt’s model.
- Describe what are Ethics and Trustworthy AI in particular.
- Recall the general definition of ethics.
- Recall that a Human Centric Ethical Purpose respects fundamental rights, principles and values.
- Recall that Ethical Purpose AI is delivered using Trustworthy AI that is technically robust.
- Recall that the Human Centric Ethical Purpose is continually assessed and monitored.
- Describe the three fundamental areas of sustainability and the United Nations’s seventeen sustainability goals.
- Describe how AI is part of “Universal Design” and “The Fourth Industrial Revolution.”
- Understanding ML is a significant contribution to the growth of Artificial Intelligence.
- Describe “learning from experience” and how it relates to Machine Learning (ML) (Tom Mitchell’s explicit definition).
- Demonstrate understanding of the AI Intelligent agent description.
- List the four rational agent dependencies.
- Describe agents in terms of performance measures, environment, actuators and sensors.
- Describe four types of agents: reflex, model-based reflex, goal-based and utility-based.
- Identify the relationship of AI agents with Machine Learning (ML)
- Describe what a robot is and
- Describe robotic paradigms.
- Describe an intelligent robot.
- Relate intelligent robotics to intelligent agents.
- Describe how sustainability relates to human-centric ethical AI and how our values will drive our use of AI to change humans, society and organizations.
- Explain the benefits of Artificial Intelligence.
- List advantages of machine and human intelligence.
- Describe the challenges of Artificial Intelligence.
- General ethical challenges AI raises.
- General examples of the limitations of AI systems compared to human systems.
- Demonstrate understanding of the risks of AI projects.
- Give at least one general example of the risks of AI.
- Describe a typical AI project team.
- Describe a domain expert.
- Describe what is “fit-of-purpose.”
- Describe the difference between waterfall and agile projects.
- List opportunities for AI.
- Identify a typical funding source for AI projects and relate it to the NASA Technology Readiness Levels (TRLs).
- Describe how we learn more from data functionality, software and hardware.
- List common open-source machine Learning functionality, software and hardware.
- Describe the introductory theory of Machine Learning.
- Describe typical tasks in preparation of data.
- Describe typical types of Machine Learning Algorithms.
- Describe the typical methods of visualizing data.
- Recall which typical, narrow AI capability is useful in ML and AI agents’ functionality.
- Demonstrate an understanding that Artificial Intelligence (in particular, Machine Learning) will drive humans and machines to work together.
- List future directions of humans and machines working together.
- Describe a “learning from experience” Agile approach to projects.
- Describe the type of team members needed for an Agile project.