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BCS Foundation Course in Artificial Intelligence

The BCS Foundation course in Artificial Intelligence is our latest Artificial Intelligence training course. The course builds upon the basic knowledge of AI. Over the 3 days the course will take you from a basic understanding of AI to the ability to create your own AI product.

This course in Artificial Intelligence incorporates and builds on the essentials certification to develop a portfolio of AI examples using the basic process of machine learning. It shows how AI delivers business, engineering and knowledge benefits.

Examples are presented; drawing on standard open source software and cloud services. Candidates will explore what is required to develop a machine learning portfolio and given access to the examples for on-going self-study.

Who is it for?

Those individuals 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 technical officers; Chief information officers; Organisational change practitioners and managers; Business change practitioners and managers; Service architects and managers; Programme and planning managers; Service providers

Select specific date to see price, venue and full details.

Learning Objectives

  1. Ethical and Sustainable Human and Artificial Intelligence; ( 25% )

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.
  1. Applying the benefits, challenges and risks of a Machine Learning project ( 30% )

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
  1. An introduction to Machine Learning Theory and Practice (35%)

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.
  1. The Management, Roles and Responsibilities of humans and machines. (10%)

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.


There are no entry requirements for this training

Course Content

Each major subject heading in this syllabus is assigned and allocated a percentage of study time. The purpose of this is:
1) Guidance on the proportion of time allocated to each section of an accredited course.
2) Guidance on the proportion of questions in the exam.

The course will cover the follow topics:

  • Ethical and Sustainable Human and Artificial Intelligence ( 25% )
  • Applying the benefits, challenges and risks of a Machine Learning project ( 30% )
  • An introduction to Machine Learning Theory and Practice (35%)
  • The Management, Roles and Responsibilities of humans and machines (10%)

Exams & Certification

The exam will consist of:

  • A one-hour closed book exam
  • Consisting of 40 multiple choice questions
  • Pass mark is 26/40
The exam is 60 mins with an additional 25% extra time allowed if English is not your first language making the exam 75mins.

(Currently awaiting accreditation by BCS)

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