Artificial Intelligence FoundationHQ7H8SHPHPHP-HQ7H8S1.0<p>In this course, students will learn to:
</p>
<ul>
<li>Describe how artificial intelligence (AI) is part of ‘Universal Design’ and ‘The Fourth Industrial Revolution’</li><li>Demonstrate understanding of the artificial intelligence (AI) intelligent agent description</li><li>Explain the benefits of artificial intelligence (AI)</li><li>Describe how we learn from data—functionality, software and hardware</li><li>Demonstrate an understanding that artificial intelligence (AI) (in particular, machine learning—ML) will drive humans and machines to work together</li><li>Describe a ‘learning from experience’ Agile approach to projects</li></ul><p>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.</p><ul>
<li>Introduction and Course Outline</li><li>Human and Artificial Intelligence—Part 1</li><li>Exercise 1</li><li>Human and Artificial Intelligence—Part 2</li><li>Ethics and Sustainability – Trustworthy AI—Part 1</li><li>Ethics and Sustainability – Trustworthy AI—Part 2</li><li>Sustainability, Universal Design, Fourth Industrial Revolution and Machine Learning</li><li>Exercise Two</li><li>Being Human, Conscious, Competent and Adaptable</li><li>Exercise Three</li><li>Applying the Benefits of AI</li><li>Applying the Benefits of AI</li><li>Building a Machine Learning Toolbox</li><li>Building a Machine Learning Toolbox</li><li>Exercise Four</li><li>Building a Machine Learning Toolbox – Two Case Studies</li><li>Building a Machine Learning Toolbox</li><li>Building a Machine Learning Toolbox</li><li>Building a Machine Learning Toolbox</li><li>A Simple Neural Network Schematic</li><li>Exercise Five</li><li>Machine Learning and Consciousness</li><li>The Future of Artificial Intelligence</li><li>Exercise Six</li><li>Learning from Experience</li><li>Conclusion</li><li>Exam Practice and Preparation</li><li>Examination</li></ul><h5>Introduction and Course Outline </h5><ul>
<li>Course overview and structure</li><li>Exam information</li><li>Daily schedule</li></ul><h5>Human and Artificial Intelligence—Part 1 </h5><ul>
<li>General definition of AI</li><li>Ethics</li><li>Sustainability</li><li>AI as part of Universal Design and The Fourth Industrial Revolution</li><li>Challenges and risks</li></ul><h5>Exercise 1 </h5><ul>
<li>Opportunities for AI</li></ul><h5>Human and Artificial Intelligence—Part 2 </h5><ul>
<li>Learning from experience</li><li>Applying the benefits of AI</li><li>Opportunities</li></ul><h5>Ethics and Sustainability – Trustworthy AI—Part 1</h5><ul>
<li>Roles and responsibilities of humans and machines</li></ul><h5>Ethics and Sustainability – Trustworthy AI—Part 2</h5><ul>
<li>Trustworthy AI</li></ul><h5>Sustainability, Universal Design, Fourth Industrial Revolution and Machine Learning</h5><ul>
<li>Learning from data, functionality, software and hardware</li></ul><h5> Exercise Two </h5><ul>
<li>Ethics and sustainability</li></ul><ul>
<li>Artificial Intelligent Agents and Robotics</li></ul><ul>
<li>AI intelligent agent description</li><li>What a robot is</li><li>What an intelligent robot is</li></ul><h5> Being Human, Conscious, Competent and Adaptable</h5><ul>
<li>AI project teams</li><li>Modelling humans</li></ul><h5>Exercise Three</h5><ul>
<li>Human plus machine mindmap</li></ul><h5>What is a Robot?</h5><ul>
<li>Definition of a robot</li><li>Robot paradigm</li></ul><h5>Applying the Benefits of AI </h5><ul>
<li>Benefits, challenges and risks</li></ul><h5>Applying the Benefits of AI </h5><ul>
<li>Opportunities and funding</li></ul><h5>Building a Machine Learning Toolbox </h5><ul>
<li>How do we learn from data?</li></ul><h5>Building a Machine Learning Toolbox </h5><ul>
<li>Types of machine learning</li></ul><h5>Exercise Four </h5><ul>
<li>Define a simple ML problem</li></ul>
<h5>Building a Machine Learning Toolbox – Two Case Studies</h5><h5>Building a Machine Learning Toolbox </h5><ul>
<li>Introduction to probability and statistics</li></ul><h5>Building a Machine Learning Toolbox</h5>
<ul>
<li>Introduction to linear algebra and vector calculus</li></ul><h5>Building a Machine Learning Toolbox</h5><ul>
<li>Visualising data</li></ul><h5>A Simple Neural Network Schematic </h5><ul>
<li>Introduction to neural networks</li></ul><h5>Exercise Five </h5><ul>
<li>Maturity and funding of an AI system</li></ul>
<h5>Open Source ML and Robotic Systems </h5><ul>
<li>Open source software for AI and robotics</li></ul><h5>Machine Learning and Consciousness </h5><ul>
<li>Introduction to machine learning and consciousness</li></ul>
<h5>The Future of Artificial Intelligence </h5><ul>
<li>The human + machine</li><li>What will drive humans and machines to work together</li></ul>
<h5>Exercise Six </h5><ul>
<li>Explore the future opportunities for AI and human systems</li></ul><h5>Learning from Experience</h5>
<ul>
<li>Agile projects</li></ul>
<h5>Conclusion</h5><h5>Exam Practice and Preparation</h5><h5>Examination</h5>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 projectsThe 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.- Introduction and Course Outline
- Human and Artificial Intelligence—Part 1
- Exercise 1
- Human and Artificial Intelligence—Part 2
- Ethics and Sustainability – Trustworthy AI—Part 1
- Ethics and Sustainability – Trustworthy AI—Part 2
- Sustainability, Universal Design, Fourth Industrial Revolution and Machine Learning
- Exercise Two
- Being Human, Conscious, Competent and Adaptable
- Exercise Three
- Applying the Benefits of AI
- Applying the Benefits of AI
- Building a Machine Learning Toolbox
- Building a Machine Learning Toolbox
- Exercise Four
- Building a Machine Learning Toolbox – Two Case Studies
- Building a Machine Learning Toolbox
- Building a Machine Learning Toolbox
- Building a Machine Learning Toolbox
- A Simple Neural Network Schematic
- Exercise Five
- Machine Learning and Consciousness
- The Future of Artificial Intelligence
- Exercise Six
- Learning from Experience
- Conclusion
- Exam Practice and Preparation
- ExaminationIntroduction 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
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
Examination3 jours2250.002250.002250.00