In this course, students will learn to:

• Describe how artificial intelligence (AI) is part of ‘Universal Design’ and ‘The Fourth Industrial Revolution’
• Demonstrate understanding of the artificial intelligence (AI) intelligent agent description
• Explain the benefits of artificial intelligence (AI)
• Describe how we learn from data—functionality, software and hardware
• Demonstrate an understanding that artificial intelligence (AI) (in particular, machine learning—ML) will drive humans and machines to work together
• Describe a ‘learning from experience’ Agile approach to projects

Introduction and Course Outline

• Course overview and structure
• Exam information
• Daily schedule

Human and Artificial Intelligence—Part 1

• General definition of AI
• Ethics
• Sustainability
• AI as part of Universal Design and The Fourth Industrial Revolution
• Challenges and risks

Exercise 1

• Opportunities for AI

Human and Artificial Intelligence—Part 2

• Learning from experience
• Applying the benefits of AI
• Opportunities

Ethics and Sustainability – Trustworthy AI—Part 1

• Roles and responsibilities of humans and machines

Ethics and Sustainability – Trustworthy AI—Part 2

• Trustworthy AI

Sustainability, Universal Design, Fourth Industrial Revolution and Machine Learning

• Learning from data, functionality, software and hardware

Exercise Two

• Ethics and sustainability

Artificial Intelligent Agents and Robotics

• AI intelligent agent description
• What a robot is
• What an intelligent robot is

Being Human, Conscious, Competent and Adaptable

• AI project teams
• Modelling humans

Exercise Three

• Human plus machine mindmap

What is a Robot?

• Definition of a robot
• Robot paradigm

Applying the Benefits of AI

• Benefits, challenges and risks

Applying the Benefits of AI

• Opportunities and funding

Building a Machine Learning Toolbox

• How do we learn from data?

Building a Machine Learning Toolbox

• Types of machine learning Exercise Four
• Define a simple ML problem

Exercise Four

• Define a simple ML problem

Building a Machine Learning Toolbox – Two Case Studies

Building a Machine Learning Toolbox

• Introduction to probability and statistics

Building a Machine Learning Toolbox

• Introduction to linear algebra and vector calculus

Building a Machine Learning Toolbox

• Visualising data

A Simple Neural Network Schematic

• Introduction to neural networks

Exercise Five

• Maturity and funding of an AI system

Open Source ML and Robotic Systems

• Open source software for AI and robotics

Machine Learning and Consciousness

• Introduction to machine learning and consciousness

The Future of Artificial Intelligence

• The human + machine
• What will drive humans and machines to work together

Exercise Six

• Explore the future opportunities for AI and human systems

Learning from Experience

• Agile projects

Conclusion

Exam Practice and Preparation

Examination

The EXIN BCS Artificial Intelligence Foundation certification is focused on individuals with an interest in (or need to implement) AI in an organization—especially those working in areas such as science, engineering, knowledge engineering, finance, education or IT services.