What Will You Learn?

Do you want a career in IT Asset Management (ITAM)? This ITAM Foundation online training course will cover the 4 key areas of IT Asset Management:

  • Hardware Asset Management (Including Mobile Devices)

  • Software Asset Management

  • Services And Cloud Asset Management

  • People And Information Asset Management (Including Bring Your Own Device)

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Course Curriculum

  • 1

    Sustainable Human & AI

    • Introduction

    • Exercise Documents

    • Journey Home - So What!

    • Chapter 1 - Introduction

    • Learning Objectives

    • Human And AI - Part 1

    • Human And AI - Part 2

    • Dictionary Definition - IQ & EQ

    • Aristotle

    • The Scientific Method - Objective

    • Emotional Intelligence - EQ - Subjective

    • The Industrial Revolutions

    • Universal Design - Design For All

    • Exercise One

    • Artificial Intelligence

    • History Of AI & Machine Learning

    • Machines Learn From Data

    • Tom Mitchell Definition Of ML

    • Heuristic - Sometimes Works

    • A Human Being Is More Than IQ & EQ

    • Go With Your Gut Feeling - Magnus Walker

    • The Digital Human

    • AI - Deep Learning

    • What Have We Learned?

    • Dilts’s Logical Levels

    • Neuro-Linguistic Programming

    • Logical Levels Of Change

    • NLP - Helps People With Change

    • AI And The Rational Agent

    • Chapter 1 - Quiz

    • AI Ethics - What Comes Before Law!

    • What Is Ethics?

    • Definitions

    • Law Vs Ethics - Part 1

    • Law Vs Ethics - Part 2

    • Artificial Intelligence Ethics

    • Ethics

    • Critical Concerns Raised By AI

    • EU Ethics Guidelines For Trustworthy AI

    • Fundamental Rights Of Human Beings

    • Future Of Life - Asilomar AI Principals

    • A Framework For Achieving Trustworthy AI

    • Fundamental Rights To Principles And Values

    • The Role Of AI Ethics - Part 1

    • The Role Of AI Ethics - Part 2

    • Domain Specific Code Of Ethics

    • Artificial Intelligence Ethics

    • Ethical Principles: AI & Corresponding Values

    • Requirements Of Trustworthy AI - Part 1

    • Requirements Of Trustworthy AI - Part 2

    • Methods To Achieve Trustworthy AI - Part 1

    • Methods To Achieve Trustworthy AI - Part 2

    • Methods To Achieve Trustworthy AI - Part 3

    • Assessing Trustworthy AI

    • In Summary

    • Sustainability, Universal Design And ML

    • What Is Sustainability?

    • Climate Change – Protecting The Planet!

    • We Need To Measure What We Are Doing!

    • United Nations Sustainability Goals

    • The Fourth Industrial Revolution

    • What Is Universal Design?

    • Biophilic Design - Architecture

    • Human Plus Machine

    • Automation

    • Tom Mitchell Definition Of ML

    • AI Can Help Build Better Models

    • Machine Learning – Part Of The AI Toolkit

    • Enablers – Machine Learning

    • Machine Learning

    • Narrow (Weak) AI

    • Exercise Two

  • 2

    AI & Robotics

    • Chapter 2 - Introduction

    • Learning Objectives

    • Artificial Intelligence & Robotics

    • Schematic Of Artificial Intelligence

    • AI Agent Expectations

    • AI Agent Structure

    • AI Agent Examples

    • Types Of Agent - Part 1

    • Types Of Agent - Part 2

    • The Learning From Experience

    • Russell & Norvig - A General Learning Agent

    • 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?

    • Playing Games – Adversarial Searching

    • Agent Based Modelling

    • Summary

    • Being Human - Part 1

    • Being Human - Part 2

    • Being Human - Part 3

    • Neuro-Linguistic Programming Tools

    • Generalization, Deletion & Distortion

    • Human Modelling

    • Logical Levels Of Change

    • Building Rapport

    • Good Rapport Leads To Better Modelling

    • NLP – Professional Qualification

    • Exercise Three

    • What Is A Robot?

    • Robot Definition

    • Robot Examples - Part 1

    • Robot Examples - Part 2

    • Autonomous Vehicles

    • Robot Paradigm - Part 1

    • Robot Paradigm - Part 2

    • Robot Paradigm Descriptions

    • Hierarchical, Reactive, Hybrid Deliberative

    • Russell & Norvig - A General Learning Agent

    • State Of The Agent World

    • How Can AI Helps Robotics?

    • Robot Mechanics Isn't Easy!

    • Examples Of Risks And Opportunities

    • Robotics Guidelines EPSRC

    • Summary

  • 3

    Benefits & Challenges Of AI

    • Chapter 3 - Introduction

    • Learning Objectives

    • Opportunities

    • AI - What Do We Need?

    • Humans And Machines Working Together

    • What Humans Do Well - Subjective

    • Automation

    • Optical Character Recognition

    • Research And Development – R & D

    • Engineering

    • Health And Social Care

    • Entertainment

    • Sales & Marketing

    • Logistics – Planning And Organisation

    • ML Enabler - Internet Of Things

    • Cloud High Performance Computing

    • Deep Learning Artificial Neural Networks

    • ML Enabler – Deep Reinforcement Learning

    • ML Classification Boosting & Clustering

    • Chapter 3 - Quiz

    • Challenges, Risks & Funding

    • Utopia Or Dystopia

    • Ethics – The Elephant In The Corner

    • ML Challenges And Risks - Part 1

    • ML Challenges And Risks - Part 2

    • ML Challenges And Risks - Part 3

    • Consciousness

    • Considerations For An AI Project

    • Funding

    • Technology Readiness Levels

    • Likely Funders

    • Chapter 3 - Quiz

  • 4

    Starting To Use AI

    • Chapter 4 - Introduction

    • Learning Objectives

    • How Do We Learn From Data?

    • Russell & Norvig - A General Learning Agent

    • State Of The Agent World

    • Typical Agent Functionality

    • ML – Part Of The AI Toolkit

    • Tom Mitchell Definition Of ML

    • Machine Learning Is Multi-Disciplinary

    • ML - Good Data And Algorithms - Part 1

    • ML - Good Data And Algorithms - Part 2

    • ML – Open Source Code

    • Stages Of A ML Project – Aurélien Géron

    • Mathematical Pillars Of ML – Gilbert Strang

    • Typical First “Hello World” ML Projects

    • Building A Machine Learning Toolbox

    • The Types Of Machine Learning - Part 1

    • The Types Of Machine Learning - Part 2

    • The Types Of Machine Learning - Part 3

    • The Types Of Machine Learning - Part 4

    • The Types Of Machine Learning - Part 5

    • The Types Of Machine Learning - Part 6

    • The Types Of Machine Learning - Part 7

    • ML - Good Data And Algorithms - Part 1

    • ML - Good Data And Algorithms - Part 2

    • ML – Good Data And Algorithms - Part 3

    • Training An Algorithm - Part 1

    • Training An Algorithm - Part 2

    • Training An Algorithm - Part 3

    • Chapter 4 - Quiz

    • Exercise Four

    • Engineers Build Models Everyday

    • AI Examples Simple To Complex

    • AI Example One - Part 1

    • AI Example One - Part 2

    • AI Example Two - Part 1

    • AI Example Two - Part 2

    • AI Example Two - Part 3

    • AI Example Two - Part 4

    • AI Example Two - Part 5

    • AI Example Two - Part 6

    • Introduction To Probability And Statistics

    • ML - Uses Probability And Statistics

    • What Is Probability?

    • Venn Diagrams - Part 1

    • Venn Diagrams - Part 2

    • Venn Diagrams - Part 3

    • Venn Diagrams - Part 4

    • Venn Diagrams - Part 5

    • Venn Diagrams - Part 6

    • Bayesian Networks

    • Statistical Learning

    • A Probability Distribution Example - Part 1

    • A Probability Distribution Example - Part 2

    • Typical PDFs

    • Central Limit Theorem Example

    • Generating Random Numbers

    • So Far...

    • Introduction To Linear Algebra - Part 1

    • Introduction To Linear Algebra - Part 2

    • Open Source Software

    • What Is Linear Algebra?

    • Vector Calculus To Linear Algebra

    • Scalars And Vectors

    • What Can We Do With Vectors?

    • Matrix – Arrays Of Numbers

    • Matrices

    • Matrix - Notation

    • Matrices In Linear Algebra

    • Linear Algebra

    • Vector Calculus - Part 1

    • Vector Calculus - Part 2

    • Vector Calculus - Part 3

    • Black-Scholes Equation

    • Summary - Linear Algebra And Vector Calculus

    • Visualising Data

    • What Is Visualising Data?

    • Stages Of A ML Project – Aurélien Géron

    • Data Visualisation

    • Typical Packages

    • Commercial Packages

    • Large Data Sets

    • Think About The Human

    • Examples - Graphs

    • Iso-Contours And Iso-Surfaces

    • Networks

    • Virtual And Augmented Reality

    • The Learning Environment - Part 1

    • The Learning Environment - Part 2

    • The Health Environment

    • Visualising Data

    • A Simple Neural Network

    • AI To ML To Neural Networks

    • Human Basis - Part 1

    • Human Basis - Part 2

    • Mathematical Model - McCullock And Pitts

    • A Network Of Perceptrons - Part 1

    • A Network Of Perceptrons - Part 2

    • Training A Neural Network

    • Russell & Norvig - A General Learning Agent

    • Typical Example Is The Control Of A Cartpole

    • A Simple Neural Network

    • Exercise Five

    • Open Source Software & Robotics

    • Open Source Software For AI & Robotics

    • Object Orientated Programming

    • Hardware – Parallel Computation

    • Example - Tensor Flow

    • Example – Scikit Learn

    • Getting Started

    • Robot Operating System (ROS)

    • Educational Lego EV3

    • Open Source Software & Robotics

    • Machine Learning And Consciousness

    • Tom Mitchell Definition Of ML

    • Consciousness

    • Many Scientists Have Published Ideas

    • Organoids

    • Professor David Chalmers – Two Questions

    • The Chinese Room Experiment

    • Strong AI Is Not Here

    • Machine Learning And Consciousness

  • 5

    Roles Of Humans & AI

    • Chapter 5 - Introduction

    • Learning Objectives

    • The Future Of AI - Human + Machine

    • Human Plus Machine

    • Fourth Industrial Revolution - Technology

    • Reinforcement Convolution Neural Networks

    • Build Intelligent Entities

    • Sustainability – Intergenerational Equity

    • Ethics In AI

    • Human Consciousness

    • Humans Provide The Subjective

    • Utopia Or Dystopia

    • Humans Only Roles

    • Humans Complement Machine

    • AI Enhances Humans

    • Humans Drive The Change

    • Asilomar Principles

    • Get The KASH

    • Chapter 5: Quiz

    • Learning From Experience – Agile Projects

    • Are AI Projects Different?

    • An Agile Or Waterfall Approach?

    • Waterfall

    • Agile

    • Agile Development

    • The Agile Manifesto

    • Agile Lends Itself To AI Projects

    • Business Case For AI Projects - Part 1

    • Business Case For AI Projects - Part 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

    • What Was In It For Me?

    • Journey Home - So What!

    • Reading List - Part 1

    • Reading List - Part 2

    • Reading List - Part 3

    • Additional Reading List

  • 6

    What Did You Learn?

    • BCS AI Foundation Mock Exam Instructions

    • BCS AI Foundation Mock Exam

    • BCS AI Foundation Exam Outline

    • How To Order Your Examination

  • 7

    How Did We Do? Tell Us...

    • AI Foundation - Survey

Frequently Asked Questions

Have you got burning questions? You might be able to find the answers for them here:

  • What Are The Prerequisites?

    There are no entry-level requirements for the BCS Artificial Intelligence (AI) Foundation training course, all of the information that is required to pass the examination is included in the courseware.

  • What Is The Examination Format?

    The examination consists of 40 multiple-choice questions and has a duration of one hour. In order to pass the examination, you must score 26/40. The examination is an online proctored exam conducted by BCS.

  • Who Should Attend?

    The ITAM Foundation training course is intended for all key personnel in the organisation involved in the IT lifecycle. Whilst this certification has an IT focus, it has been specifically designed for non-IT personnel in mind.

  • Do I Need Identification For The Examination?

    You will be required to have some form of photo ID (passport, government ID or equivalent) for the examination. If the examination includes a document to be printed, then please ensure that you have a printer in the room where you will be sitting the exam. Some examination institutes will ask you to have a small mirror or reflective surface and the proctor will request that you hold this up to show there is nothing attached to the webcam or even the laptop itself. Also, Adobe Flash Player and Shockwave must also be installed on your desktop or laptop prior to the examination.

  • What Are The Technical Requirements?

    You will need access to a laptop with headphones, or speakers.