Foundation Certificate in Artificial Intelligence (FCAI)
Live Instructor Led. Face-to-Face or Attend-From-Any-Where
What is included?
- 3 days of training
- Course material/Slides
- Examination Fees
- 95.8% Certification Success in First Attempt
- Classroom training Or Attend-From-Any-Where
- Training delivered by Professionals with enormous industry experience
- Total comprehensive exam preparation
What you will Learn?
- Ethical and sustainable human and artificial intelligence
- Artificial Intelligence and robotics
- Applying the benefits of AI – challenges and risks
- Starting AI: how to build a Machine Learning toolbox – theory and practice
- The management, role and responsibilities of humans and machines
Award-winning training that you can trust
Who should attend?
Engineers, scientists, organisational change practitioners, service architects, program and planning managers, web developers, chief technical officers, service provider portfolio strategists / leads, business strategists and consultants.
Anyone with an interest in (or need to implement) artificial intelligence in an organisation, especially those working in areas such as science, engineering, knowledge engineering, finance, education or IT services.
Ethical and Sustainable Human and Artificial Intelligence (20%)
Candidates will be able to:
1.1. Recall the general definition of Human and Artificial Intelligence (AI).
1.1.1. Describe the concept of intelligent agents.
1.1.2. Describe a modern approach to Human logical levels of thinking using
Robert Dilt’s Model.
1.2. Describe what are Ethics and Trustworthy AI, in particular:
1.1.1. Recall the general definition of Ethics.
1.2.1. Recall that a Human Centric Ethical Purpose respects fundamental rights,
principles and values.
1.2.2. Recall that Ethical Purpose AI is delivered using Trustworthy AI that is
1.2.3. Recall that the Human Centric Ethical Purpose Trustworthy AI is
continually assessed and monitored.
1.3. Describe the three fundamental areas of sustainability and the United Nation’s
seventeen sustainability goals.
1.4. Describe how AI is part of ‘Universal Design,’ and ‘The Fourth Industrial
1.5. Understand that ML is a significant contribution to the growth of Artificial
1.5.1. Describe ‘learning from experience’ and how it relates to Machine Learning
(ML) (Tom Mitchell’s explicit definition).
- Artificial Intelligence and Robotics (20%)
2.1. Demonstrate understanding of the AI intelligent agent description, and:
2.1.1. list the four rational agent dependencies.
2.1.2. describe agents in terms of performance measure, environment, actuators
2.1.3. describe four types of agent: reflex, model-based reflex, goal-based and
2.1.4. identify the relationship of AI agents with Machine Learning (ML).
2.2. Describe what a robot is and:
2.2.1. Describe robotic paradigms,
2.3. Describe what an intelligent robot is and:
2.3.1. Relate intelligent robotics to intelligent agents.
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BCS Foundation Certificate in Artificial Intelligence
V1.0 Oct 2019
- Applying the benefits of AI – challenges and risks (15%)
3.1. Describe how sustainability relates to human-centric ethical AI and how our values
will drive our use of AI will change humans, society and organisations.
3.2. Explain the benefits of Artificial Intelligence by.
3.2.1. list advantages of machine and human and machine systems.
3.3. Describe the challenges of Artificial Intelligence, and give;
3.3.1. general ethical challenges AI raises.
3.3.2. general examples of the limitations of AI systems compared to human
3.4. Demonstrate understanding of the risks of AI project, and:
3.4.1. give at least one a general example of the risks of AI,
3.4.2. describe a typical AI project team in particular,
18.104.22.168. describe a domain expert,
22.214.171.124. describe what is ‘fit-of-purpose’,
126.96.36.199. describe the difference between waterfall and agile projects.
3.5. List opportunities for AI.
3.6. Identify a typical funding source for AI projects and relate to the NASA Technology
Readiness Levels (TRLs).
- Starting AI how to build a Machine Learning Toolbox – Theory and Practice (30%)
4.1. Describe how we learn from data – functionality, software and hardware,
4.1.1. List common open source machine learning functionality, software and
4.1.2. Describe introductory theory of Machine Learning.
4.1.3. Describe typical tasks in the preparation of data.
4.1.4. Describe typical types of Machine Learning Algorithms.
4.1.5. Describe the typical methods of visualising data.
4.2. Recall which typical, narrow AI capability is useful in ML and AI agents’
- The Management, Roles and Responsibilities of humans and machines (15%)
5.1. Demonstrate an understanding that Artificial Intelligence (in particular, Machine
Learning) will drive humans and machines to work together.
5.2. List future directions of humans and machines working together.
5.3. Describe a ‘learning from experience’ Agile approach to projects
5.3.1. Describe the type of team members needed for an Agile project.