Master the Bias-Variance Trade-off in Machine Learning 📊

In this video we discuss why underfitting and overfitting happen in machine learning models. We look at the fundamental theory behind bias and variance, and try to derive the results from there.

*Notes*
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Between 03:30 - 03:45, on the bottom card, the parenthesis should say "Low Variance", not "High Variance" as it is in the video. Sorry for any confusion that this might have caused.

*Related Videos*
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Why neural networks are universal functions approximators: https://youtu.be/O45AaRPQhuI
Bagging vs Boosting: https://youtu.be/tjy0yL1rRRU
Why neural networks are deep: https://youtu.be/Wss3WMy5OxE
Why residual connections work: https://youtu.be/Gey9CG6R6w8
Why we need activations in neural nets: https://youtu.be/rj6K46u0J5w

*Contents*
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00:00 - Intro
00:28 - Bias and Variance
01:45 - Bias and Variance Example
02:35 - The Bias Variance Trade-off
03:14 - Bias and Variance in Neural Nets/Linear Regression
04:32 - Outro

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#bias #variance #underfitting #overfitting #biasvariancetradeoff