BCS Foundation Cert. in AI & Machine Learning

BCS Foundation Certificate in Artificial Intelligence

Learn the terminology and general principles of AI, 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.

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Course Details

Take the next step in developing your knowledge and understanding of artificial intelligence (AI) 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.

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.

European students can purchase this course in Euros. Please allow 1 working day for enrolment.

Course curriculum

1. Welcome
  FREE PREVIEW
2. AI Foundation Course Syllabus
3. How to use this course
  FREE PREVIEW
4. Reading List
5. Optional Reading List
6. Before you begin...
1. Contents
2. What Is Artificial Intelligence (AI)?
3. What Is Human Intelligence?
4. IQ & EQ
5. Aristotle
6. The Scientific Method
7. Timeline of AI & Machine Learning (ML)
8. Industrial Revolutions
9. Universal Design
10. Machines Learn From Data
11. Tom Mitchell's Definition of ML
12. Heuristic
13. A Human is More Than IQ & EQ
14. Identifying Objects
15. The Digital Human
16. Human Brain Inspired AI 'Deep Learning' (DL)
17. Test Your Learning - Part One
18. Summary
1. Contents
2. Introducing Robert Dilt
3. Dilt & Neuro Linguistic Programming (NLP)
4. Introducing Neuro Linguistic Programming (NLP)
5. Introducing Neuro Linguistic Programming (NLP)
6. Robert Dilt’s NLP Logical Levels of Change
7. NLP Helping Individuals & Groups With Change
8. AI & the Rational Agent
9. Summary
1. Contents
2. AI Ethics
3. What is ethics?
4. Definitions in Ethics
5. Law v Ethics
6. Organisations Working with Ethics in AI
7. Critical Concerns Raised by AI
8. EU ethics guidelines for trustworthy AI (EU)
9. Fundamental Rights of Human Beings
10. Future of Life – Asilomar AI Principles
11. Summary
1. Contents
2. From Fundamental Rights to Principles and Values - What is the Aim?
3. The Role of AI ethics
4. A Domain-specific Code of Ethics
5. The EU Guidelines for Ethical AI
6. Ethical Principles in the Context of AI and Corresponding Values
7. Requirements of Trustworthy AI
8. Technical and Non-Technical Methods to Achieve Trustworthy AI
9. Technical and Non-technical Methods to Achieve Trustworthy AI
10. Assessing Trustworthy AI
11. Summary
1. Contents
2. What is Sustainability?
3. Climate Change – Protecting the Planet!
4. We Need to Measure What We Are Doing
5. United Nations Sustainability Goals
6. The Fourth Industrial Revolution – Founder of the World Economic Forum
7. The Possible Fifth Industrial Revolution
8. What is Universal Design (design for all)?
9. Example of Universal Design in Architecture
10. Human Plus Machine
11. Recap – Formal Tom Mitchell Definition of ML
12. Patrick Winston from MIT
13. Machine Learning is Part of the AI Toolkit
14. What Have Been the Enablers for Machine Learning do we Need?
15. Skills Needed for Machine Learning
16. Narrow (weak) AI – Very Successful Today
17. Summary
1. Learning Outcomes
2. Contents
3. Schematic of Artificial Intelligence
4. Agent Structure – Architecture and Program
5. Agent Real-World Examples
6. We Assume Agents Have the Following
7. State of the Agent World
8. Typical Agent Functionality
9. What is State of the Art?
10. Autonomy – the AI agent and a Robot!
11. What About Multiple Learning Agents?
12. Multi-agent Environments. Playing games Using Adversarial Searching
13. Agent-Based Modelling
14. Summary
1. Contents
2. Humans – Objective and Subjective, Conscious and Subconscious
3. NLP - How Do We Model a Human Who’s Good at Something?
4. NLP Approaches
5. Modelling a Human
6. NLP Example – Building Rapport
7. Good Rapport Leads to Better Modelling
8. NLP – Professional Qualification
9. Summary
1. Contents
2. What is a Robot, AI Robotics & Autonomy?
3. Early Examples of Robots
4. Modern-Day Examples of Robots
5. Robot Examples - Autonomous vehicles
6. Technology Advancing - Autonomous vehicles
7. Robot Paradigm
8. Robot Paradigm
9. Choosing a Robot Paradigm
10. Robot Paradigm Descriptions
11. Reminder of Russell and Norvig’s Schematic
12. Recap: State of the Agent World
13. Has AI helped robotics, or has robotics helped AI?
14. Creating Robot Mechanics Isn’t Easy
15. EPSRC Robotics Guidelines
16. Summary
1. Contents
2. Key Steps in an AI/ML Project
3. Humans and Machines Working Together
4. What Humans and Machines Do Well (subjective)
5. What is Automation?
6. OCR - Optical Character Recognition
7. Research and Development (R&D)
8. Engineering
9. Health Care and Social Care
10. Entertainment
11. Sales and Marketing
12. Logistics – Planning and Organisation
13. ML Enabler – Internet of Things – Big Data
14. ML Enabler – Cloud High Performance Computing (HPC)
15. ML Enabler – Deep Learning Artificial Neural Networks
16. ML Enabler – Deep Reinforcement Learning
17. Popular Machine Learning Techniques
18. Summary
1. Content
2. Will Our Future be Utopia, Dystopia or in Between?
3. Reminder: Ethics
4. ML challenges and risks (1/3)
5. ML challenges and Risks (2/3)
6. ML Challenges and Risks (3/3)
7. Consciousness in Machines
8. List of Considerations Before Developing an AI Project
9. Funding
10. Technology Readiness Levels 1/2
11. Technology Readiness Levels 2/2
12. Likely Funders 1/2
13. Likely Funders 2/2
14. Test your learning
15. Summary
1. Contents - Building a Machine Learning Toolbox
2. Recap: Russell and Norvig – a General Learning Agent
3. Recap: Russell and Norvig
4. Recap: State of the Agent World
5. Recap: State of the Agent World
6. Typical Agent Functionality
7. Machine Learning – Part of the AI toolkit
8. Formal Tom Mitchell Definition of ML
9. Machine Learning is Multi-Disciplinary
10. Machine learning – Good Data and Algorithms
11. Good Data and Algorithms
12. Good Data and Algorithms: Overfitting
13. Good Data and Algorithms: Underfitting
14. Good Data and Algorithms: Underfitting
15. FREE Open Source Code & Cheap High-Performance Cloud Computing
16. Stages of a Machine Learning Project – Aurélien Géron
17. The Mathematical Pillars of Machine Learning – Gilbert Strang
18. Typical First “hello world” ML Projects
19. Test your learning
20. Summary
1. Contents
2. Types of ML
3. Batch and Offline Learning
4. Online Learning
5. Instance Based and Model Based Learning
6. Training an Algorithm 1/4
7. Training an Algorithm 2/4
8. Training an Algorithm 3/4
9. Training an Algorithm 4/4
10. Recap: Engineers Build Models Every Day
11. Test Your Learning
12. Two AI Case Studies: Simple to Complex
13. AI Case Study – Thermal Store Decay
14. AI Case Study: Operational Research
15. AI Case Study One: Summary
16. AI Case Study Two - Modelling of Atmospheric Turbulence
17. AI Case Study Two - Modelling of Atmospheric Turbulence
18. AI Case Study Two - Modelling of Atmospheric Turbulence
19. AI Case Study Two - Modelling of Atmospheric Turbulence
20. AI Case Study Two - Modelling of Atmospheric Turbulence
21. AI Case Study Two - Modelling of Atmospheric Turbulence
22. AI Case Study Two - Modelling of Atmospheric Turbulence
23. AI Case Study Two - Modelling of Atmospheric Turbulence
24. AI Case Study Two - Modelling of Atmospheric Turbulence
25. AI Case Study Two - Modelling of Atmospheric Turbulence Results
26. Summary
1. Content
2. Machine Learning Uses Probability
3. What is Probability?
4. What is Probability?
5. Venn diagrams – Pictorial or Intuitive Representation of Probability 1/2
6. Venn Diagrams – Pictorial or Intuitive Representation of Probability 2/2
7. Axiom 3
8. Definition of Conditional Probability
9. Definition of Statistical Independence
10. Definition of Conditional Independence
11. Bayesian Networks
12. Statistical Learning
13. What is a Probability Distribution?
14. What is a Probability Distribution?
15. Typical Probability Density Functions
16. Central Limit Theorem - Probability of Rolling a 6
17. Generating Random Numbers
18. Recap – Take a Breather
1. Contents
2. An Introduction to Linear Algebra and Vector Calculus
3. An Abundance of Software
4. What is Linear Algebra and Vector Calculus?
5. Vector calculus to Linear Algebra
6. Scalars and Vectors
7. Matrix – Arrays of Numbers
8. What Can we do with Vectors?
9. Matrices
10. Matrix – Notation
11. Matrices in Linear Algebra Help Us
12. Linear Algebra
13. Vector Calculus 1/3
14. Vector Calculus 2/3
15. Vector Calculus 3/3
16. Black–Scholes Equation
17. Recap: An Introduction to Linear Algebra and Vector Calculus
1. Contents
2. What is Visualising Data?
3. Stages of a machine learning project – Aurélien Géron
4. Big Data - Large Data Sets
5. Think About the Human
6. Examples – Graphs
7. Iso-Contours and Iso-Surfaces
8. Networks
9. Virtual and Augmented Reality
10. The Learning Environment
11. Human & Machine: Creating a Learning Environment
12. Human & Machine: Robotic Surgery
13. Human & Machine: Robotic Surgery
14. Recap: Visualising data
15. Summary
1. Contents
2. AI to Machine Learning to Neural Networks
3. Human Basis of the Neural Network
4. Human Basis – Neural Networks Forming an Interconnected Network
5. Mathematical Model of How Human Brains Work Electrically
6. A Network of Perceptrons 1/2
7. A Network of Perceptrons 2/2
8. Training a Neural Network
9. Recap: Russell and Norvig - a General Learning Agent
10. Robotic Control Example
11. Summary
1. Contents
2. Open Source and Commercial Data Visualisation
3. Commercial Open-Source Packages
4. Open-Source Software for AI and Robotics
5. Object-Orientated Programming
6. Hardware – Parallel Computation
7. Starting a Project: Cloud Computing
8. Starting a Project: Robotics
9. Open-source: Code Tensorflow
10. Open-Source Code: Scikit-Learn
11. Starting a Project: Lego EV3
12. Summary
1. Contents
2. Recap: Formal Tom Mitchell definition of ML
3. Consciousness
4. Many Scientists Have Published Ideas
5. Human Brain Cells Grown into Computers (organoids)
6. Professor David Chalmers – Two Questions
7. Professor John Searle – the Chinese Room Experiment
8. Summary
1. Contents
2. Human Plus Machine
3. Fourth Industrial Revolution – Technology
4. Reinforcement Convolutional Neural Networks (RCNN)
5. Building Intelligent Entities
6. Sustainability – Intergenerational Equity
7. Recap: Ethics in AI
8. Recap: Human Consciousness
9. Humans Provide the Subjective
10. Recap: Utopia or Dystopia or Somewhere
11. Human-Only Roles
12. Human Complements Machine
13. How AI Enhances Humans
14. Humans Drive the Change
15. Recap: Asilomar Principles
16. Get the KASH
17. Summary
1. Contents
2. Checklist to Decide an Agile or Waterfall Approach?
3. Waterfall
4. Agile
5. Agile Development
6. The Agile Manifesto
7. Agile Lends Itself to AI Projects
8. Business Case for AI Projects (1)
9. Business Case for AI projects (2)
10. Getting Started – Team Members
11. A DevOps approach – “Agile & DevOps”
12. A DevOps approach – “the benefits”
13. A DevOps approach – “bringing AI to life”
14. A DevOps Approach – “the 3 ways”
15. Sustainability Feedback Throughout the Project
16. Summary
1. Course Conclusion
2. Before you go...
1. Reading List
2. Optional Reading List
1. Preparation for the exam (Updated September 2021)
  FREE PREVIEW
2. BCS Foundation AI Specimen Test Paper and Answers

About this course

£599.00
310 lessons
Tutor support and student forum
Includes exam cost
12 months access

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?

A qualified tutor is on hand to answer any questions you have about the course and the content, via email or the student discussion forum.
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 BCS Accredited Trainer Darren Winter

Darren Winter is the Founder and Company Director of Duco Digital the leading online UK BCS (Chartered Institute for IT) accredited provider of Artificial Intelligence & Machine Learning, Business Analysis and Data Protection courses for individuals and businesses. He is a BCS accredited trainer and a regular guest speaker about technology, and champion of small business.

Testimonials

Good Training Materials

Tom

Good training materials for remote learning, and the most impressive part was the speed at which they responded to questions. I also had issues with the exam (through the exam platform, not Duco) and they were really helpful in resolving it.

Great Courses

Gemma

I'm currently working through their AI courses and am looking forward to working my way through each of their new online courses as they are released.

Enjoyable Course

Flaminia

I really enjoyed this course, thank you so much.

Valuable Course

Janet

This proved to be a very valuable course to me.

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For only £599. Price includes 12 months access, tutor support and exam costs.

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Course reviews

BCS Foundation Certificate in Artificial Intelligence

Iwan Kurnianto Wibowo

Very good course. Help me to understand about AI

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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.

Are you looking for a live course?

Let us know if you would like this course to be taught to you live. We will contact you to confirm availability.

Welcome to BCS Foundation in Artificial Intelligence

Human & Artificial Intelligence Part One

Human & Artificial Intelligence Part Two

Ethics & Sustainability, Roles & Responsibilities of Humans & Machines

Ethics & Sustainability, Trustworthy AI

Sustainability, universal design, fourth industrial revolution and machine learning

Artificial Intelligence & Robotics

Being Human, Conscious, Competent and Adaptable

What is a Robot?

Applying the benefits of AI

Applying the Benefits of AI - Opportunities & Funding

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

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

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

An Introduction to Linear Algebra and Vector Calculus

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

A Simple Neural Network Schematic - Introduction to Neural Networks

Open Source ML & Robotic Systems

Machine Learning & Consciousness

The Future of Artificial Intelligence

Learning From Experience

Conclusion

Reading List

Exam Preparation

About this course

Who is it for?

What will I learn?

Exam

Entry requirements

What Do I Get?

How Long Does It Take to Complete?

What Tutor Support Is Available?

What Career Support Is Available?

Good Training Materials

Great Courses

Enjoyable Course

Valuable Course