Course Summary
Our Artificial Intelligence (AI) Essentials training course will give you a basic understanding of the terminology and concepts used by those working in Artificial Intelligence programs and projects.
Artificial Intelligence (AI) is a methodology for using a non-human system to learn from experience and imitate human intelligent behaviour. The essentials certificate tests a candidate’s knowledge and understanding of the terminology and the general principles of AI.
1. Artificial & Human Intelligence: Introduction & History (25%)
You 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 Know the general description of human and Artificial Intelligence
1.4.2 Describe ‘learning from experience’ and how it relates to Machine Learning – Tom Mitchell’s explicit definition
1.4.3 Know that ML is a significant contribution to Artificial Intelligence
1.4.4 Describe how AI is part of ‘Universal Design,’ and ‘The Fourth Industrial Revolution’
2. Examples Of AI: Benefits, Challenges & Risks (30%)
You will be able to:
2.1 Explain the benefits of Artificial Intelligence and list advantages of the 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 general example of the risks of AI
2.3.1 Know the benefits of Artificial Intelligence
12.3.1.1 List advantages of machine and human and machine systems
2.3.2 Know the challenges of Artificial Intelligence
2.3.2.1 Give general examples of the limitations of AI compared to human systems
2.3.2.2 Give general ethical challenges AI raises
2.3.3 Know the risks of Artificial Intelligence
2.3.3.1 Give a general example of the risks of AI
2.3.3.2 Identify a typical funding source for AI projects
2.3.4 List opportunities for AI
3. Introduction To Machine Learning (35%)
You 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 Understand the AI intelligent agent description and can identify the differences with Machine Learning (ML)
3.3.1.1 List the four rational agent dependencies
3.3.1.2 Describe agents in terms of Performance Measure, Environment, Actuators and Sensors
3.3.1.3 Describe four types of agent, Reflex, Model-based reflex, Goal-based agent and Utility-based agent.
3.3.2 Know typical examples of Machine Learning in business, social ( media, entertainment ) and science
3.3.3 Know what typical narrow AI functionality is useful in ML and AI agents functionality
3.3.4 Describe and give examples of supervised, un-supervised, semi-supervised and reinforcement machine learning
3.3.5 Describe the basic schematic of a Neural Network
4.The Future of Artificial Intelligence – Human and Machine Together (10%)
You 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
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