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When Models Meet Data

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08When Models Meet Data

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Why this matters in ML

Empirical risk, generalization and the bias–variance trade-off explain why models that fit training data perfectly can still fail on new data — and how to fix it.

train/test split
regularization
cross-validation

Step 42
~20m2Code · 15
⚡Challenge
Data, Models, and Learning
The previous seven chapters built a mathematical toolbox. Chapter 8 turns the key and opens the door to machine learning proper. Three ingre…
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Step 43
~20m3D · 12Code · 25
★Most cinematic⚡Challenge
Parameter Estimation (MLE and MAP)
Probabilistic models recast learning as parameter estimation. Assume the data is generated by — a family of distributions indexed by . Given…
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Step 44
~20m3D · 12Code · 15
★Most cinematic⚡Challenge
Probabilistic Modeling and Model Selection
Once you accept a probabilistic view, every modeling decision — the functional form, the noise distribution, the prior — is a modeling assum…
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When Models Meet Data

Let’s figure it out.

Data, Models, and Learning

Parameter Estimation (MLE and MAP)

Probabilistic Modeling and Model Selection

When Models Meet Data

Let’s figure it out.

Data, Models, and Learning

Parameter Estimation (MLE and MAP)

Probabilistic Modeling and Model Selection