CMU 10-301 HW6: Learning Theory, MLE/MAP, and Fairness Metrics

TL;DR HW6 combines generalization, MLE/MAP, probabilistic learning, fairness metrics, and social impact in one written assignment about assumptions and tradeoffs.

#cmu #machine-learning #learning-theory #ai-ethics

Table of Contents

One spine: why trust a model
First check and completion
References

Series: Reading CMU 10-301 Machine Learning (6 / 10)

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The PDF in the official HW6 ZIP is titled Homework 6: Learning Theory, MLE/MAP, Fairness Metrics, and Societal Impact; the coursework index shortens this to Learning Theory and Ethics. It is entirely written. Sections cover learning theory, MLE/MAP, probabilistic learning, fairness metrics, societal impacts and unintended consequences, and society/ethics/ML. The ZIP includes a PDF, LaTeX template, fairness CSV, and figures, but no starter code or reference answers.

One spine: why trust a model

Learning theory asks when a sample supports population claims. MLE and MAP expose the roles of data and priors. Fairness metrics ask how errors are distributed. None is a one-number verdict; assumptions, objectives, and affected groups come first.

First check and completion

Open the fairness_dataset.csv inside the ZIP and identify columns, groups, labels, and predictions before computing a metric. Completion means listing assumptions for every bound/estimator, reproducing fairness calculations from the CSV, and separating mathematically supported results from value judgments in societal-impact responses. There are no official answers to claim as verification.

CMU 10-301 HW6: Learning Theory, MLE/MAP, and Fairness Metrics

One spine: why trust a model

First check and completion

References

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