Artificial Intelligence Foundation Certificate Training Course

Course Outline

AI Foundation level Certificate

Syllabus Learning Objectives

1. Ethical and Sustainable Human and Artificial Intelligence; ( 25% )

Candidates will be able to:

1.1 Recall the general definition of human and Artificial Intelligence (AI);

1.2 Describe ‘learning from experience’ and how it relates to Machine Learning (ML) (Tom Mitchell’s explicit definition);

1.3 Understand that ML is a significant contribution to the growth of Artificial Intelligence;

1.4 Describe how AI is part of ‘Universal Design,’ and ‘The Fourth Industrial Revolution’.

1.5 Describe a modern approach to human logical levels of thinking using Robert Dilt’s Model.

1.6 Describe the three fundamental areas of sustainability.

Applying the benefits, challenges and risks of a Machine Learning project ( 30% )

Candidates will be able to:

2.1 Explain the benefits of Artificial Intelligence, and

2.1.1 list advantages of machine and human and machine systems;

2.2 Describe the challenges of Artificial Intelligence, and give:

2.2.1 general examples of the limitations of AI compared to human systems,

2.2.2 general ethical challenges AI raises.

2.3 Demonstrate understanding of the risks of Artificial Intelligence, and

2.3.1 give at least one a general example of the risks of AI;

2.4 Identify a typical funding source for AI projects;

2.5 List opportunities for AI.

2.6 Describe how sustainability relates to AI and how our values will drive our use of AI and how our values will change our society and organisations.

3. An introduction to Machine Learning Theory and Practice (35%)

Candidates will be able to:

3.1 Demonstrate understanding of the AI intelligent agent description, and:

3.1.1 identify the differences with Machine Learning (ML), and:
3.1.2 list the four rational agent dependencies,
3.1.3 describe agents in terms of performance measure, environment, actuators and sensors,
3.1.4 describe four types of agent: reflex, model-based reflex, goal-based and utility-based.

3.2 Give typical examples of Machine Learning in the following contexts:

3.2.1 business,
3.2.2 social (media, entertainment),
3.3.3 science.

3.3 Recall which typical, narrow AI capability is useful in ML and AI agents’ functionality;

3.4 Recall the basic theory of ML.

3.5 Describe the basic schematic of a neutral network.

3.6 Know how to build a practical Machine Learning Toolkit.

4.0 The Management, Roles and Responsibilities of humans and machines. (10%)

Candidates will be able to:

4.1 Demonstrate an understanding that Artificial Intelligence (in particular, Machine Learning) will drive humans and machines to work together;

4.2 List future directions of humans and machines working together.

4.3 Describe a ‘learning from experience’ Agile approach to projects:

4.3.1 Describe the type of team members needed for an Agile project.