Section outline

  • ·         Overview: Beyond overfitting, many practical pitfalls can trip you up when applying ML in the real world. This lesson covers a variety of common mistakes and issues: using bad data, evaluation errors (like testing incorrectly), distribution changes when deploying, ethical biases, and blindly trusting models. By recognizing these pitfalls, students will learn to avoid them and build more reliable ML systems.

    • Micro-Topic 10.1: “Garbage In, Garbage Out” – Data Pitfalls – Goal: Stress the importance of data quality and quantity.

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    • Micro-Topic 10.2: Validation & Evaluation Mistakes – Goal: Identify common errors in testing models and interpreting results.

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    • Micro-Topic 10.3: Deployment & Data Drift Pitfalls – Goal: Discuss problems that arise when moving a model from the lab to the real world.

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    • Micro-Topic 10.4: Ethical Pitfalls – Bias and Fairness – Goal: Make students aware of bias in ML and the importance of fairness.

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    • Micro-Topic 10.5: Over-Reliance on ML and Lack of Interpretability – Goal: Warn against blindly trusting models and discuss the need for explainability.

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