# ML Pipeline Resources

## Knowledge

- [An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani)](https://www.statlearning.com/)
  Free official PDF + R/Python labs. Use for: the formal treatment behind linear regression, kNN, classification, and model evaluation — this is the standard companion text for a course like STOR 323.
- [StatQuest with Josh Starmer — YouTube channel](https://www.youtube.com/@statquest/videos)
  Short, visual, intuition-first explainers. Use for: building intuition on softmax/logistic regression, kNN, confusion matrices, and ROC/AUC before or after reading the formal slides. Search the channel for the specific topic (e.g. "ROC and AUC", "K-nearest neighbors").
- [Fairness and Machine Learning: Limitations and Opportunities — Barocas, Hardt, Narayanan](https://fairmlbook.org/)
  Free draft of the MIT Press book, used in many university courses. Use for: the "equal rule ≠ equal burden" idea (slide 20) — group fairness metrics (FPR/FNR by group), and the deeper legal/philosophical framing the slide gestures at but doesn't unpack.

## Wisdom (Communities)

- No community preference stated yet. Will revisit if/when the user wants a place to test understanding (e.g. r/statistics, r/MachineLearning, or a UNC STOR 323 study group) — not proposing proactively until asked.

## Gaps
- No STOR 323-specific syllabus, textbook, or course website located yet — only the Lecture 3 slide deck has been supplied. If the course has an official textbook or posted syllabus, share it so citations can point students back to exactly what the course expects.