# Authoritative Readings and Resources

These resources extend the supplied learning chapters in **AINS6002 Machine Learning & Predictive Modeling**. They were selected because they are primary standards, official documentation, open textbooks, or authoritative institutional guidance—not unsourced link lists.

## How to Read Them

For each module, read the supplied chapter first. Then use the two linked resources at the end of that chapter to test terminology, compare the course's worked example with an authoritative treatment, and identify one point that should change or qualify your recommendation. Students are not expected to read every linked document cover to cover.

### 1. [Google Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course)

Interactive explanations of core predictive-modeling concepts.

**Use with:** Prediction tasks and data framing, Model selection and validation, Unsupervised learning and structure discovery, Predictive modeling portfolio.
### 2. [scikit-learn User Guide](https://scikit-learn.org/stable/user_guide.html)

Authoritative implementation and evaluation guidance.

**Use with:** Prediction tasks and data framing, Data preparation and feature pipelines, Unsupervised learning and structure discovery, Time, drift, and monitoring.
### 3. [scikit-learn Model Evaluation](https://scikit-learn.org/stable/modules/model_evaluation.html)

Metrics, scoring, validation, and error analysis.

**Use with:** Data preparation and feature pipelines, Linear and tree-based baselines, Time, drift, and monitoring, Interpretability and stakeholder explanation.
### 4. [NIST AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework)

Risk framing for predictive systems in practice.

**Use with:** Linear and tree-based baselines, Model selection and validation, Interpretability and stakeholder explanation, Predictive modeling portfolio.

## Source-Use Standard

Assignments should distinguish among measured notebook evidence, course-provided synthetic evidence, claims supported by these sources, and the student's own professional judgment. Cite the specific page, section, control, or documentation topic used; a bare homepage link is not adequate evidence.