The APMG Foundation Certificate in Artificial Intelligence
Course 1298
3 DAY COURSE

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.

APMG Foundation in Artificial Intelligence Certification Benefits

• Course Benefits:

  • 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.

• Training Prerequisites

  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

Module 1: Ethical and Sustainable Human and Artificial Intelligence

• 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).

Module 2: Artificial Intelligence and Robotics

• 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.

Module 3: Applying the benefits of AI – challenges and risks

• 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).

Module 4: Starting AI and how to build a Machine Learning Toolbox – Theory and Practice

• 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.

Module 5: The Management, Roles and Responsibilities of humans and machines

• 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.