Course Outline:

Lesson 1: Describing the subsets of AI

• 1.1.1 Define Artificial Intelligence and how it relates to problem solving
• 1.1.2 Describe how algorithms are used in AI
• 1.1.3 Explain what an algorithm consists of and how they are used in problem solving
• 1.1.4 Define “Big Data” and examples of it in today’s world
• 1.1.5 Describe some everyday examples of AI and their purposes
• 1.1.6 Describe AIs significant impact in different areas

Lesson 2: Subsets and History of AI

• 2.1: DESCRIBING THE SUBSETS OF AI
• 2.1.2 Describe how these subsets are connected
• 2.1.3 Explain why machine learning is the most used area of AI
• 2.1.4 Explain the difference between machine learning and deep learning
• 2.2: Describe how AI has developed over time
• 2.2.1 Create a timeline of the development of AI
• 2.2.2 Identify who the word “Artificial Intelligence” was first coined by and when
• 2.2.3 Identify milestones in the development of AI
• 2.2.4 Describe some examples of how AI has been used over time (Product Examples)
• 2.2.5 What are some international laws and ethics regulations regarding the use of AI

Lesson 3: AI Types Based on Technology

• 3.1: TYPES OF AI ACCORDING TO TECHNOLOGY
• 3.1.1 Identify the three types of AI that are divided by technology
• 3.1.2 Explain why narrow AI is the only one achieved so far
• 3.1.3 Describe some examples of narrow AI
• 3.1.4 Explain what Natural Language Processing is and how it provides a personalized experience
• 3.1.5 Explain how narrow AI can be reactive or have limited memory
• 3.1.6 Describe examples of narrow AI in today’s world
• 3.1.7 Define what factors make AI considered to be “Deep AI” type
• 3.1.8 Explain how Deep AI is different from Narrow AI
• 3.1.8 Define Artificial Super Intelligence

Lesson 4: AI Types Based on Functionality

• 4.1.1 IDENTIFY THE FOUR TYPES OF AI THAT ARE DIVIDED BY FUNCTIONALITY
• 4.1.2 Describe what a reactive machine can and cannot do
• 4.1.4 Explain how reactive machines work.
• 4.1.5 Describe some everyday examples reactive machines
• 4.1.6 Define what the limited memory class of machines are.
• 4.1.7 Explain how the “Theory of Mind” machines are for the future and are different from reactive and limited memory
• machines
• 4.1.8 Explain how machines with self-awareness are the final future step of AI

Lesson 5: Machine Learning in AI

• 5.1: HOW DOES MACHINE LEARNING FIT INTO AI
• 5.1.2 Define machine learning
• 5.1.2 Describe how artificial intelligence applies machine learning
• 5.1.3 Identify the four stages of machine learning training
• 5.1.4 Explain how data collection is the first step in ML
• 5.1.5 Identify examples of machine learning
• 5.1.6 Identify examples of machine learning
• 5.1.7 Explain how machine learning works
• 5.2: DESCRIBE THREE CATEGORIES OF MACHINE LEARNING
• 5.2.1 Define supervised learning
• 5.2.2 Define unsupervised learning
• 5.2.3 Define reinforcement learning
• 5.2.4 Describe how machines use data differently in each category of machine learning

Lesson 6: AI and Robotics

• 6.1: AI AND ROBOTICS TOGETHER
• 6.1.1 Explain how is AI and robots work together
• 6.1.2 Identify examples of robots that use AI
• 6.1.3 Describe how robots use AI accomplish tasks
• 6.1.4 Explain how robots help people in different areas of life
• 6.1.5 Identify different types of robots

Lesson 7: The Future of AI and Careers

• 7.1.1 Explain why the “Theory of Mind” AI will be in the future
• 7.1.2 Explain how AI will help solve problems
• 7.1.3 Define deep neural networks
• 7.2 DESCRIBE SOME CAREERS IN AI
• 7.2.1. Identify careers that use AI
• 7.2.2 Explain some soft skills that people in AI careers will need to be successful
• 7.2.3 Explain ways career fields will be impacted by AI
• 7.2.4 Describe the skills and background needed to have a career in AI
• 7.2.5 Describe Career Paths in AI
• 7.2.6 Identify some companies that hire AI Professionals

Lesson 8: Legal and Ethical considerations

• 8.1.1 Identify what ethical considerations will need to continue to be addressed in AI in the future
• 8.1.2 Explain some security issues that arise with AI
• 8.1.3 Explain what “algorithmic bias” means.
• 8.1.4 Describe how training data affects the accuracy of supervised machine learning
• 8.1.5 Identify privacy issues involved with AI
• 8.1.6 Explain how culture, beliefs and religion can create bias/conflict in AI
• 8.1.7 Define what ethical guidelines, organizations and principles that govern them

All necessary course materials are included.