Artificial Intelligence (AI) Foundation Training Course

Course Outline

AI Foundation Certification Course Outline

Syllabus and Learning Objectives

1. Ethical And Sustainable Human And Artificial Intelligence (25%)

After this section of the AI Certification course is complete, participants will gain an understanding of the following:

• Recall the general definition of Human Intelligence and Artificial Intelligence
• Make parallels between ‘learning from experience’ and Machine Learning (ML) via Tom Mitchell’s definition
• Understand that ML is a significant contribution to the growth of AI
• Describe how AI is part of Universal Design and The Fourth Industrial Revolution
• Describe a modern approach to human logical levels of thinking using Robert Dilts’ Model.
• Describe the three fundamental areas of sustainability.

2. Applying the Benefits, Challenges, And Risks of Machine Learning (30%)

At the end of this section, participants have completed over half of the course material. By now, and in combination with what they’ve learned in the previous section about AL and ML, they will be able to apply and understand the following:

• Explain the benefits of Artificial Intelligence
• list advantages of machine and human-machine systems
• Describe the challenges of Artificial Intelligence
• general examples of AI limitations compared to human systems
• general ethical challenges AI raises
• Demonstrate understanding of the risks of Artificial Intelligence
• give at least one general example of the risks of AI
• Identify a typical funding source for AI projects
• List opportunities for AI
• Describe how sustainability relates to AI and how our values will drive use of AI and change our society.

3. An introduction To Machine Learning Theory And Practice (35%)

At the completion of the section of the AI Foundation training course, participants will be able to apply their knowledge from the previous sections and understand the following:

• Demonstrate an understanding of the AI intelligent agent description
• Identify the differences between AI and ML
• List the four rational agent dependencies
• Describe agents in terms of performance measure, environment, actuators, and sensors
• Describe four types of agent: reflex, model-based reflex, goal-based, and utility-based
• Give typical examples of Machine Learning in the following contexts
  • business,
  • social (media, entertainment),
  • science.
• Recall which typical, narrow AI capability is useful in ML and AI agents’ functionality
• Recall the basic theory of ML
• Describe the basic schematic of a neural network
• Know how to build a practical Machine Learning Toolkit

4. The Management, Roles And Responsibilities Of Humans And Machines. (10%)

Completing this section finalises the participant’s understanding of all aspects required to pass the AI Foundation Certification course. In this final section, participants understand:

• 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