Who Should Attend?

Those with an interest in, or need to implement, AI in an organisation, especially those working in areas such as science, engineering, knowledge engineering, finance, or IT services.

The following broad set of roles would be interested:

Engineers; Scientists; Professional research managers; Chief Technology Officers; Chief Information Officers; Organisational change practitioners and managers; Business change practitioners and managers; Service architects and managers; Programme and planning managers; and Service providers.

Exam Details

The exam will consist of a one-hour, closed book exam consisting of 40 multiple choice questions. The pass mark is 26/40.

(Currently awaiting accreditation by BCS).


Over three days, the BCS Foundation in Artificial Intelligence course will take you from a basic understanding of AI to the ability to create your own AI product. It incorporates and builds on the essentials certification to develop a portfolio of AI examples using the basic process of Machine Learning.

Choose a location

03 - 05 November 2020

Course Content and Agenda

The course consists of four modules, spread over three days.

Candidates will be able to:

  • Recall the general definition of human and Artificial Intelligence (AI)
  • Describe ‘learning from experience’ and how it relates to Machine Learning (ML) (Tom Mitchell’s explicit definition)
  • Understand that ML is a significant contribution to the growth of Artificial Intelligence
  • Describe how AI is part of ‘Universal Design,’ and ‘The Fourth Industrial Revolution’
  • Describe a modern approach to human logical levels of thinking using Robert Dilt’s Model
  • Describe the three fundamental areas of sustainability

Candidates will be able to:

  • Explain the benefits of Artificial Intelligence, and;
    • List advantages of machine and human and machine systems.
  • Describe the challenges of Artificial Intelligence, and give:
    • General examples of the limitations of AI compared to human systems,
    • General ethical challenges AI raises.
  • Demonstrate understanding of the risks of Artificial Intelligence, and
    • Give at least one a general example of the risks of AI
    • Identify a typical funding source for AI projects
    • List opportunities for AI
  • 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

Candidates will be able to:

  • Demonstrate understanding of the AI intelligent agent description, and
    • Identify the differences with Machine Learning (ML), and
    • 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
  • Give typical examples of Machine Learning in the following contexts:
    • Business
    • Social (media, entertainment)
  • Recall which typical, narrow AI capability is useful in ML and AI agents’ functionality
  • Recall the basic theory of ML
  • Describe the basic schematic of a neutral network
  • Know how to build a practical Machine Learning Toolkit

Candidates will be able to:

  • 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
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