Artificial Intelligence (AI) Essentials Training Course

Course Outline

Learning Objectives

1.Artificial and Human Intelligence: an Introduction and History (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);
1.3 Understand what enabled the growth of Artificial Intelligence;
1.4 Describe how Machine Learning is part of Universal Design and The Fourth Industrial Revolution.

1.4.1 The candidate knows the general description of human and Artificial Intelligence;

1.4.2 The candidate can describe ‘learning from experience’ and how it relates to Machine Learning – Tom Mitchell’s explicit definition;

1.4.3 The candidate knows that ML is a significant contribution to Artificial Intelligence;

1.4.4 The candidate can describe how AI is part of ‘Universal Design,’ and ‘The Fourth Industrial Revolution’.

2.Examples of AI: Benefits, Challenges and Risks (30%)

Candidates will be able to:

2.1 Explain the benefits of Artificial Intelligence and list advantages of machine and human and machine systems;

2.2 Describe the challenges of Artificial Intelligence and give: general examples of the limitations of AI compared to human systems, general ethical challenges AI raises;

2.3 Demonstrate understanding of the risks of Artificial Intelligence and give: at least one a general example of the risks of AI.

2.3.1 The candidate knows the benefits of Artificial Intelligence;

12.3.1.1 and also list advantages of machine and human and machine systems;

2.3.2 The candidate knows the challenges of Artificial Intelligence;

2.3.2.1 and also give general examples of the limitations of AI compared to human systems,

2.3.2.2 and also give general ethical challenges AI raises;

2.3.3 the candidate knows the risks of Artificial Intelligence;

2.3.3.1.and also give a general example of the risks of AI;

2.3.3.2 the candidate can identify a typical funding source for AI projects.

2.3.4 The candidate can list opportunities for AI.

3.An introduction to Machine Learning (35%)

Candidates will be able to:

3.1 Demonstrate understanding of the approach to a Machine Learning project and list and describe each stage;

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

Business,

Social (media, entertainment),

Science;

3.3 Describe both what and where the resources for Machine Learning are.

3.3.1 the candidate understands the AI intelligent agent description and can identify the differences with Machine Learning ( ML );

3.3.1.1 can list the four rational agent dependencies,

3.3.1.2 can describe agents in terms of Performance Measure, Environment, Actuators and Sensors,

3.3.1.3 can describe four types of agent, Reflex, Model-based reflex, Goal-based agent and Utility-based agent.

3.3.2 the candidate knows typical examples of Machine Learning in business, social ( media, entertainment ) and science;

3.3.3 the candidate knows what typical narrow AI functionality is useful in ML and AI agents functionality;

3.3.4 the candidate can describe and give examples of supervised, un-supervised, semi-supervised and reinforcement machine learning;

3.3.5 the candidate can describe the basic schematic of a Neural Network.

4.The Future of Artificial Intelligence – Human and Machine Together (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.