AI_L2_2

T·

AI Learning

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SubjectAI Learning

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🧩 AI Worksheet – Week 2: Missing Data & Unbalanced Training 🧠 1. Let’s Remember! (Warm-Up Recap) Last week, we learned that AI learns from examples — we give it input, it learns patterns, and gives an output. 👉 This week, we’ll see what happens when AI doesn’t get enough or fair examples. 🎯 2. Learning Goals By the end of this lesson, you will be able to: Explain what missing or unbalanced data means. Understand how it makes AI “confused” or “unfair.” Train and test a model with balanced and...

This worksheet explores the concepts of missing data and unbalanced data in AI training. It provides foundational knowledge on how AI learns from examples, illustrating the impact of both missing and unbalanced data through engaging scenarios, such as training an AI to recognize fruits. Key learning goals include explaining the meaning of these data types and understanding their implications on AI performance. Students conduct a hands-on activity where they create an unbalanced dataset, train a model, and analyze its performance. The worksheet emphasizes the importance of balanced data for effective AI learning and encourages reflection on the observed results.