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CMU 10-301 HW9: Close the Course with Ensembles, k-Means, PCA, and Recommenders

Aug 22, 2026 1 min
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TL;DR The final written assignment combines ensembles, clustering, representation, and recommendation to test whether you can choose a learning paradigm from problem structure.
#cmu#machine-learning#clustering#pca#recommender-systems
Table of Contents
  1. The common question
  2. First check and completion
  3. References
Series: Reading CMU 10-301 Machine Learning (9 / 10)
CMU 10-301 HW8: From MDPs to Reinforcement-Learning Updates

🌏 中文版

The official handout is titled Homework 9: Learning Paradigms and is the final, entirely written assignment. Its four main sections are PCA, k-Means, Ensemble Methods, and Recommender Systems. The ZIP supplies the PDF, LaTeX template, figures, and a few plotting data files, but no starter code, full dataset, or reference answers.

The common question

Ensembles combine predictors; k-means groups unlabeled examples by distance; PCA preserves major variation; recommenders exploit sparse user-item signals. Each first requires an objective, representation, and failure conditions.

First check and completion

No programming command is required. First open the ZIP's LaTeX template and figures/data, and map each artifact to its PCA, k-means, or recommender section before calculating. Completion means hand-running a k-means assignment/update, calculating a two-dimensional PCA direction, explaining ensemble bias/variance tradeoffs and recommendation sparsity, and drawing a course-wide method-selection map. There are no official answers against which to claim verification.

References

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