BCS Foundation Certificate in Artificial Intelligence

On this accelerated BCS Foundation Certificate in Artificial Intelligence course, you will take the next step in developing your knowledge and understanding of artificial intelligence with this new certificate. Learn the general principles of AI, its potential implications and capabilities and how to assess AI products and services from multiple angles.

In just 3 days, you’ll also gain an understanding of:

• 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

Module 1: Ethical and Sustainable Human and Artificial Intelligence (20%)

• Recall the general definition of Human and Artificial Intelligence (AI).
  • Describe the concept of intelligent agents.
  • Describe a modern approach to Human logical levels of thinking using Robert Dilt’s Model.
• Describe what are Ethics and Trustworthy AI, in particular:
  • Recall the general definition of ethics.
  • Recall that a Human Centric Ethical Purpose respects fundamental rights, principles, and values.
  • Recall that Ethical Purpose AI is delivered using Trustworthy AI that is technically robust.
  • Recall that the Human Centric Ethical Purpose Trustworthy AI is continually assessed and monitored.
• Describe the three fundamental areas of sustainability and the United Nation’s seventeen sustainability goals.
• Describe how AI is part of ‘Universal Design,’ and ‘The Fourth Industrial Revolution’.
• Understand that ML is a significant contribution to the growth of Artificial Intelligence.
• Describe ‘learning from experience’ and how it relates to Machine Learning (ML)

Module 2: Artificial Intelligence and Robotics (20%)

• Demonstrate understanding of the AI intelligent agent description:
  • 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.
  • identify the relationship of AI agents with Machine Learning (ML).
• Describe what a robot is:
  • Describe robotic paradigms,
• Describe what an intelligent robot is and:
  • Relate intelligent robotics to intelligent agents.

Module 3: Applying the benefits of AI - challenges and risks (15%)

• 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.
• Explain the benefits of Artificial Intelligence by.
  • list advantages of machine and human and machine systems.
• Describe the challenges of Artificial Intelligence, and give;
  • general ethical challenges AI raises.
  • general examples of the limitations of AI systems compared to human systems.
• Demonstrate understanding of the risks of AI project, and:
  • give at least one a general example of the risks of AI,
  • describe a typical AI project team in particular,
  • describe a domain expert,
  • describe what is ‘fit-of-purpose’,
  • describe the difference between waterfall and agile projects.
• List opportunities for AI.
• Identify a typical funding source for AI projects and relate to the NASA Technology Readiness Levels (TRLs).

Module 4: Starting AI how to build a Machine Learning Toolbox - Theory and Practice (30%)

• Describe how we learn from data – functionality, software, and hardware,
  • List common open-source machine learning functionality, software, and hardware.
  • Describe introductory theory of Machine Learning.
  • Describe typical tasks in the preparation of data.
  • Describe typical types of Machine Learning Algorithms.
  • Describe the typical methods of visualising data.
• Recall which typical, narrow AI capability is useful in ML and AI agents’ functionality

Module 5: The Management, Roles and Responsibilities of humans and machines (15%)

•  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.

The exam is as follows;

• Duration: 60 Minutes
• Format: Multiple choice questions
• Number of questions: 40
• Passing score: 26/40 65%
• Domains:
  • Ethical and sustainable human and artificial intelligence 20%
  • Artificial intelligence and robotics 20%
  • Applying the benefits of AI - challenges and risks 15%
  • Starting AI how to build a machine learning toolbox - theory and practice 30%
  • The management, roles and responsibilities of humans and machines 15%

There are no prerequisites for this accelerated course.

Your accelerated course includes: 

• Exam vouchers
• Practice tests
• Certification Guarantee*
• Up to 8 hours Instructor led training each day
• Digital courseware

*Exam pass guarantee - pass your exam first time or come back for free training until you do.

This course is ideal for:

• 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.