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CMU 10-301 HW3: Compare K-NN, Perceptron, and Linear Regression

Aug 22, 2026 1 min
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TL;DR HW3 is written work: a decision-tree review followed by K-NN, Perceptron, and Linear Regression through inductive bias, errors, and model selection.
#cmu#machine-learning#knn#perceptron#linear-regression
Table of Contents
  1. Do not read it as four formula sheets
  2. First check and completion
  3. References
Series: Reading CMU 10-301 Machine Learning (3 / 10)
CMU 10-301 HW2: From Information Calculations to a Complete Decision Tree CMU 10-301 HW4: Turn Logistic Regression Likelihood into a Classifier

🌏 中文版

The official handout is titled Homework 3: Decision Trees, K-NN, Perceptron, Regression and is entirely written. Its sections are decision tree revisited, regression tree, k-nearest neighbors, perceptron, and linear regression, with overfitting, error rates, and model selection inside them. The ZIP contains the PDF, LaTeX template, and figures—no starter code, dataset, or reference output.

Do not read it as four formula sheets

The common question is which model fails first when representation or decision boundaries change. K-NN distance, perceptron separability, regression loss, and tree split preferences encode different assumptions.

First check and completion

There is no necessary safe command. Open the PDF/LaTeX bundle and list the assumptions behind all five sections before calculating. Completion means pairing every result with its assumption and predicting model behavior on XOR, an outlier-contaminated line, and sparse high-dimensional points. With no official answers, do not label your derivations officially verified.

References

Related

CMU 10-301 HW1: Find ML Foundation Gaps with Mathematics and Python 1 min CMU 10-301 HW2: From Information Calculations to a Complete Decision Tree 1 min CMU 10-301 HW4: Turn Logistic Regression Likelihood into a Classifier 1 min