Course Details

The BCS Foundation Certificate in Artificial Intelligence tests a candidate’s knowledge and understanding of the terminology and general principles of AI. This syllabus covers the potential benefits and challenges of Ethical and Sustainable Robust Artificial Intelligence; the basic process of Machine Learning (ML) – Building a Machine Learning Toolkit; the challenges and risks associated with an AI project, and the future of AI and Humans in work. The Foundation Certificate includes and expands on the knowledge taught in the BCS Essentials Certificate in AI.

Course curriculum

  • 1

    Welcome to BCS Foundation in Artificial Intelligence

  • 2

    Human & Artificial Intelligence Part One

    • Contents

    • What Is Artificial Intelligence (AI)?

    • What Is Human Intelligence?

    • IQ & EQ

    • Aristotle

    • The Scientific Method

    • Timeline of AI & Machine Learning (ML)

    • Industrial Revolutions

    • Universal Design

    • Machines Learn From Data

    • Tom Mitchell's Definition of ML

    • Heuristic

    • A Human is More Than IQ & EQ

    • Identifying Objects

    • The Digital Human

    • Human Brain Inspired AI 'Deep Learning' (DL)

    • Test Your Learning - Part One

    • Summary

  • 3

    Human & Artificial Intelligence Part Two

    • Contents

    • Introducing Robert Dilt

    • Dilt & Neuro Linguistic Programming (NLP)

    • Introducing Neuro Linguistic Programming (NLP)

    • Introducing Neuro Linguistic Programming (NLP)

    • Robert Dilt’s NLP Logical Levels of Change

    • NLP Helping Individuals & Groups With Change

    • AI & the Rational Agent

    • Summary

  • 4

    Ethics & Sustainability, Roles & Responsibilities of Humans & Machines

    • Contents

    • AI Ethics

    • What is ethics?

    • Definitions in Ethics

    • Law v Ethics

    • Organisations Working with Ethics in AI

    • Critical Concerns Raised by AI

    • EU ethics guidelines for trustworthy AI (EU)

    • Fundamental Rights of Human Beings

    • Future of Life – Asilomar AI Principles

    • Summary

  • 5

    Ethics & Sustainability, Trustworthy AI

    • Contents

    • From Fundamental Rights to Principles and Values - What is the Aim?

    • The Role of AI ethics

    • A Domain-specific Code of Ethics

    • The EU Guidelines for Ethical AI

    • Ethical Principles in the Context of AI and Corresponding Values

    • Requirements of Trustworthy AI

    • Technical and Non-Technical Methods to Achieve Trustworthy AI

    • Technical and Non-technical Methods to Achieve Trustworthy AI

    • Assessing Trustworthy AI

    • Summary

  • 6

    Sustainability, universal design, fourth industrial revolution and machine learning

    • Contents

    • What is Sustainability?

    • Climate Change – Protecting the Planet!

    • We Need to Measure What We Are Doing

    • United Nations Sustainability Goals

    • The Fourth Industrial Revolution – Founder of the World Economic Forum

    • The Possible Fifth Industrial Revolution

    • What is Universal Design (design for all)?

    • Example of Universal Design in Architecture

    • Human Plus Machine

    • Recap – Formal Tom Mitchell Definition of ML

    • Patrick Winston from MIT

    • Machine Learning is Part of the AI Toolkit

    • What Have Been the Enablers for Machine Learning do we Need?

    • Skills Needed for Machine Learning

    • Narrow (weak) AI – Very Successful Today

    • Summary

  • 7

    Artificial Intelligence & Robotics

    • Learning Outcomes

    • Contents

    • Schematic of Artificial Intelligence

    • Agent Structure – Architecture and Program

    • Agent Real-World Examples

    • We Assume Agents Have the Following

    • State of the Agent World

    • Typical Agent Functionality

    • What is State of the Art?

    • Autonomy – the AI agent and a Robot!

    • What About Multiple Learning Agents?

    • Multi-agent Environments. Playing games Using Adversarial Searching

    • Agent-Based Modelling

    • Summary

  • 8

    Being Human, Conscious, Competent and Adaptable

    • Contents

    • Humans – Objective and Subjective, Conscious and Subconscious

    • NLP - How Do We Model a Human Who’s Good at Something?

    • NLP Approaches

    • Modelling a Human

    • NLP Example – Building Rapport

    • Good Rapport Leads to Better Modelling

    • NLP – Professional Qualification

    • Summary

  • 9

    What is a Robot?

    • Contents

    • What is a Robot, AI Robotics & Autonomy?

    • Early Examples of Robots

    • Modern-Day Examples of Robots

    • Robot Examples - Autonomous vehicles

    • Technology Advancing - Autonomous vehicles

    • Robot Paradigm

    • Robot Paradigm

    • Choosing a Robot Paradigm

    • Robot Paradigm Descriptions

    • Reminder of Russell and Norvig’s Schematic

    • Recap: State of the Agent World

    • Has AI helped robotics, or has robotics helped AI?

    • Creating Robot Mechanics Isn’t Easy

    • EPSRC Robotics Guidelines

    • Summary

  • 10

    Applying the benefits of AI

    • Contents

    • Key Steps in an AI/ML Project

    • Humans and Machines Working Together

    • What Humans and Machines Do Well (subjective)

    • What is Automation?

    • OCR - Optical Character Recognition

    • Research and Development (R&D)

    • Engineering

    • Health Care and Social Care

    • Entertainment

    • Sales and Marketing

    • Logistics – Planning and Organisation

    • ML Enabler – Internet of Things – Big Data

    • ML Enabler – Cloud High Performance Computing (HPC)

    • ML Enabler – Deep Learning Artificial Neural Networks

    • ML Enabler – Deep Reinforcement Learning

    • Popular Machine Learning Techniques

    • Summary

  • 11

    Applying the Benefits of AI - Opportunities & Funding

    • Content

    • Will Our Future be Utopia, Dystopia or in Between?

    • Reminder: Ethics

    • ML challenges and risks (1/3)

    • ML challenges and Risks (2/3)

    • ML Challenges and Risks (3/3)

    • Consciousness in Machines

    • List of Considerations Before Developing an AI Project

    • Funding

    • Technology Readiness Levels 1/2

    • Technology Readiness Levels 2/2

    • Likely Funders 1/2

    • Likely Funders 2/2

    • Test your learning

    • Summary

  • 12

    Starting AI – How to Build a Machine Learning Toolbox - How Do We Learn From Data?

    • Contents - Building a Machine Learning Toolbox

    • Recap: Russell and Norvig – a General Learning Agent

    • Recap: Russell and Norvig

    • Recap: State of the Agent World

    • Recap: State of the Agent World

    • Typical Agent Functionality

    • Machine Learning – Part of the AI toolkit

    • Formal Tom Mitchell Definition of ML

    • Machine Learning is Multi-Disciplinary

    • Machine learning – Good Data and Algorithms

    • Good Data and Algorithms

    • Good Data and Algorithms: Overfitting

    • Good Data and Algorithms: Underfitting

    • Good Data and Algorithms: Underfitting

    • FREE Open Source Code & Cheap High-Performance Cloud Computing

    • Stages of a Machine Learning Project – Aurélien Géron

    • The Mathematical Pillars of Machine Learning – Gilbert Strang

    • Typical First “hello world” ML Projects

    • Test your learning

    • Summary

  • 13

    Starting AI – How to Build a Machine Learning Toolbox - Types of Machine Learning

    • Contents

    • Types of ML

    • Batch and Offline Learning

    • Online Learning

    • Instance Based and Model Based Learning

    • Training an Algorithm 1/4

    • Training an Algorithm 2/4

    • Training an Algorithm 3/4

    • Training an Algorithm 4/4

    • Recap: Engineers Build Models Every Day

    • Test Your Learning

    • Two AI Case Studies: Simple to Complex

    • AI Case Study – Thermal Store Decay

    • AI Case Study: Operational Research

    • AI Case Study One: Summary

    • AI Case Study Two - Modelling of Atmospheric Turbulence

    • AI Case Study Two - Modelling of Atmospheric Turbulence

    • AI Case Study Two - Modelling of Atmospheric Turbulence

    • AI Case Study Two - Modelling of Atmospheric Turbulence

    • AI Case Study Two - Modelling of Atmospheric Turbulence

    • AI Case Study Two - Modelling of Atmospheric Turbulence

    • AI Case Study Two - Modelling of Atmospheric Turbulence

    • AI Case Study Two - Modelling of Atmospheric Turbulence

    • AI Case Study Two - Modelling of Atmospheric Turbulence

    • AI Case Study Two - Modelling of Atmospheric Turbulence Results

    • Summary

  • 14

    Starting AI – How to Build a Machine Learning Toolbox - Introduction to Probability & Statistics

    • Content

    • Machine Learning Uses Probability

    • What is Probability?

    • What is Probability?

    • Venn diagrams – Pictorial or Intuitive Representation of Probability 1/2

    • Venn Diagrams – Pictorial or Intuitive Representation of Probability 2/2

    • Axiom 3

    • Definition of Conditional Probability

    • Definition of Statistical Independence

    • Definition of Conditional Independence

    • Bayesian Networks

    • Statistical Learning

    • What is a Probability Distribution?

    • What is a Probability Distribution?

    • Typical Probability Density Functions

    • Central Limit Theorem - Probability of Rolling a 6

    • Generating Random Numbers

    • Recap – Take a Breather

  • 15

    An Introduction to Linear Algebra and Vector Calculus

    • Contents

    • An Introduction to Linear Algebra and Vector Calculus

    • An Abundance of Software

    • What is Linear Algebra and Vector Calculus?

    • Vector calculus to Linear Algebra

    • Scalars and Vectors

    • Matrix – Arrays of Numbers

    • What Can we do with Vectors?

    • Matrices

    • Matrix – Notation

    • Matrices in Linear Algebra Help Us

    • Linear Algebra

    • Vector Calculus 1/3

    • Vector Calculus 2/3

    • Vector Calculus 3/3

    • Black–Scholes Equation

    • Recap: An Introduction to Linear Algebra and Vector Calculus

  • 16

    Starting AI – How to Build a Machine Learning Toolbox - Visualising Data

    • Contents

    • What is Visualising Data?

    • Stages of a machine learning project – Aurélien Géron

    • Big Data - Large Data Sets

    • Think About the Human

    • Examples – Graphs

    • Iso-Contours and Iso-Surfaces

    • Networks

    • Virtual and Augmented Reality

    • The Learning Environment

    • Human & Machine: Creating a Learning Environment

    • Human & Machine: Robotic Surgery

    • Human & Machine: Robotic Surgery

    • Recap: Visualising data

    • Summary

  • 17

    A Simple Neural Network Schematic - Introduction to Neural Networks

    • Contents

    • AI to Machine Learning to Neural Networks

    • Human Basis of the Neural Network

    • Human Basis – Neural Networks Forming an Interconnected Network

    • Mathematical Model of How Human Brains Work Electrically

    • A Network of Perceptrons 1/2

    • A Network of Perceptrons 2/2

    • Training a Neural Network

    • Recap: Russell and Norvig - a General Learning Agent

    • Robotic Control Example

    • Summary

  • 18

    Open Source ML & Robotic Systems

    • Contents

    • Open Source and Commercial Data Visualisation

    • Commercial Open-Source Packages

    • Open-Source Software for AI and Robotics

    • Object-Orientated Programming

    • Hardware – Parallel Computation

    • Starting a Project: Cloud Computing

    • Starting a Project: Robotics

    • Open-source: Code Tensorflow

    • Open-Source Code: Scikit-Learn

    • Starting a Project: Lego EV3

    • Summary

  • 19

    Machine Learning & Consciousness

    • Contents

    • Recap: Formal Tom Mitchell definition of ML

    • Consciousness

    • Many Scientists Have Published Ideas

    • Human Brain Cells Grown into Computers (organoids)

    • Professor David Chalmers – Two Questions

    • Professor John Searle – the Chinese Room Experiment

    • Summary

  • 20

    The Future of Artificial Intelligence

    • Contents

    • Human Plus Machine

    • Fourth Industrial Revolution – Technology

    • Reinforcement Convolutional Neural Networks (RCNN)

    • Building Intelligent Entities

    • Sustainability – Intergenerational Equity

    • Recap: Ethics in AI

    • Recap: Human Consciousness

    • Humans Provide the Subjective

    • Recap: Utopia or Dystopia or Somewhere

    • Human-Only Roles

    • Human Complements Machine

    • How AI Enhances Humans

    • Humans Drive the Change

    • Recap: Asilomar Principles

    • Get the KASH

    • Summary

  • 21

    Learning From Experience

    • Contents

    • Checklist to Decide an Agile or Waterfall Approach?

    • Waterfall

    • Agile

    • Agile Development

    • The Agile Manifesto

    • Agile Lends Itself to AI Projects

    • Business Case for AI Projects (1)

    • Business Case for AI projects (2)

    • Getting Started – Team Members

    • A DevOps approach – “Agile & DevOps”

    • A DevOps approach – “the benefits”

    • A DevOps approach – “bringing AI to life”

    • A DevOps Approach – “the 3 ways”

    • Sustainability Feedback Throughout the Project

    • Summary

  • 22

    Conclusion

    • Course Conclusion

    • Before you go...

  • 23

    Reading List

    • Reading List

    • Optional Reading List

  • 24

    Exam Preparation

Did You Know?

There are currently around 300,000 AI professionals worldwide but millions of roles available requiring AI skills which are only set to increase year on year (Forbes. 2018). Stay ahead of the curve and learn about how AI can impact the business world whilst developing your career in this expanding field.

Questions

  • Who is it for?

    If you are planning or currently working on an AI or Machine Learning project; looking to upskill; changing career; planning or need entry to HE or University; Directors, Project Managers & Developers.

  • What will I learn?

    A deeper understanding of AI & Machine Learning covering universal design, EU guidelines on ethical AI, how to 'build' a machine learning toolbox, introduction to robotic paradigms, neural networks, open-source software, vector calculus, algebra, the challenges of human consciousness and much more...

  • Exam

    A 40 question multiple-choice exam, included in price (pass mark is 65%) which you can take at home, computer with webcam required. You will have 12 months access to the course to help you prepare.

  • Entry requirements

    There are no entry requirements for this certification. We strongly recommend beginners to study the reading list.

  • What Do I Get?

    As well as gaining new skills and knowledge, upon successful completion, you will receive a globally recognised professional certification awarded by BCS at SFIA Plus Level 3, and free annual Associate membership to BCS to further develop your career and grow your professional network.

  • How Long Does It Take to Complete?

    We recommend around 60 hours of study before taking the exam, though many students complete the course in only a few days.

  • What Tutor Support Is Available?

    We provide free email support with any questions you have about the course.

  • What Career Support Is Available?

    When you pass your course, you will receive a one-year free membership to BCS (worth £90) which offers professional networking and career development opportunities within specific IT fields, and within BCS there is a specific careers centre ’Springboard'.

Instructor

Company Director and Accredited Trainer

Darren Winter

Darren is the Company Director of Duco Digital, a digital marketing & training business delivering professional marketing and IT solutions using Artificial Intelligence and Machine Learning. He holds an MA with merit in Cross-Cultural Communication and International Marketing and a BSc with Honors in Computing where he studied Cyber Security, AI, Networks & Communications.

What If I Change My Mind?

If you change your mind about your course purchase, you can apply for a refund within 14 days of purchase, provided that you haven't accessed your course material. Please note that you are no longer eligible if you have accessed your course material or communicated with any course tutor.