Understanding Artificial Intelligence (AI)

• Science
• Secondary Education

Understanding Artificial Intelligence (AI)

Learning Objectives

At the end of the lesson, you will be able to explain the basic processes involved in AI such as learning, reasoning, and self-correction. At the end of the lesson, you will understand the steps involved in creating and training an AI system. At the end of the lesson, you will be able to distinguish between different types of AI learning: supervised, unsupervised, and reinforcement learning.

What do you already know about Artificial Intelligence?

Introduction to AI and its Human Intelligence Simulation Processes

Artificial Intelligence (AI) simulates human intelligence through machines using learning, reasoning, and self-correction.

The Workflow of AI Systems

Includes data collection, preprocessing, and feature extraction.

Overview of Model Selection, Training, Evaluation, Deployment, and Monitoring in AI

AI systems need data collection, preprocessing, and feature extraction to work effectively. Various models like neural networks and deep learning are selected based on the task. The AI model is trained with labeled data, evaluated for accuracy, and deployed for real-world applications.

Different Learning Processes in AI

Supervised Learning: A type of AI learning where the model is trained on labeled data to learn a mapping from inputs to outputs. 

Unsupervised Learning

Unsupervised Learning: A type of AI learning where the model identifies patterns and structures in unlabeled data without guidance. 

Reinforcement Learning

Reinforcement Learning: A type of AI learning where the model learns optimal actions through trial and error, receiving rewards or penalties.

Summary and Recap

Recap of the main topics and learning objectives.

Definition List

AI: A field of computer science that simulates human intelligence processes in machines, particularly computer systems. Data Preprocessing: The process of cleaning and organizing raw data to improve its quality before it's fed into an AI model. Feature Extraction: The selection and transformation of the most relevant data attributes for use in model training. Model Selection: Choosing the appropriate AI model tailored to the specific data characteristics and task at hand. Training: The iterative process of adjusting an AI model's parameters to minimize errors in its predictions using labeled data. Evaluation: Assessing an AI model's performance on a separate dataset to determine its accuracy and generalizability. Supervised Learning: A type of AI learning where the model is trained on labeled data to learn a mapping from inputs to outputs. Unsupervised Learning: A type of AI learning where the model identifies patterns and structures in unlabeled data without guidance. Reinforcement Learning: A type of AI learning where the model learns optimal actions through trial and error, receiving rewards or penalties.

Write down 3 things you learned in this lesson.

Have students enter three things they learned in this lesson. With this they can indicate their own learning efficiency of this lesson.

Write down 2 things you want to know more about.

Here, students enter two things they would like to know more about. This not only increases involvement, but also gives them more ownership.

Ask 1 question about something you haven't quite understood yet.

The students indicate here (in question form) with which part of the material they still have difficulty. For the teacher, this not only provides insight into the extent to which the students understand/master the material, but also a good starting point for the next lesson.