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Resources: Module 01 — Introduction to AI and Machine Learning

Curated resources specifically relevant to this module's content: the AI/ML taxonomy, the ML workflow, and the bias-variance tradeoff.

Primary References

  1. scikit-learn: Getting Started Guide (https://scikit-learn.org/stable/getting_started.html) The official introduction to scikit-learn's Estimator API. Covers fit/predict/transform, pipelines, and the basic workflow this module practices. Read this alongside the hands-on examples.

  2. "Hands-On Machine Learning" by Aurélien Géron — Chapter 1: The Machine Learning Landscape The best single-chapter introduction to AI vs. ML vs. DL, the types of ML, and the workflow. Read before or after this module. (O'Reilly, 3rd edition, 2022. Verify URL via O'Reilly learning platform.)

  3. "Computing Machinery and Intelligence" by Alan Turing (1950) (https://www.cs.ox.ac.uk/activities/turing/scanned/Computing%20Machinery%20and%20Intelligence.pdf) The founding document of AI. Short (14 pages), readable, and historically fascinating. Turing proposes the Turing Test and anticipates many modern debates about machine intelligence.

For the Bias-Variance Tradeoff

  1. scikit-learn: Bias-Variance Decomposition (https://scikit-learn.org/stable/auto_examples/ensemble/plot_bias_variance.html) An illustrative example with visualizations showing how different estimators trade off bias and variance. Run the code yourself and study the plots.

  2. "Understanding the Bias-Variance Tradeoff" by Scott Fortmann-Roe (http://scott.fortmann-roe.com/docs/BiasVariance.html) A visual, intuitive explanation with a well-known "target/bullseye" diagram. Accessible to beginners. Verify the URL is current before sharing.

Historical Context

  1. "The Unreasonable Effectiveness of Data" by Halevy, Norvig, and Pereira (2009) An influential Google paper arguing that large datasets can compensate for simple models. Relevant context for the modern "data-first" view in ML. [Verify URL: Google Research or IEEE Xplore]

  2. The Wikipedia article on the History of Artificial Intelligence (https://en.wikipedia.org/wiki/History_of_artificial_intelligence) A thorough overview of the AI winters, the symbolic AI era, and the neural network revival. Useful for understanding why the field evolved the way it did.

For the Environment Setup

  1. "Python Data Science Handbook" by Jake VanderPlas — Chapter 1: IPython and Jupyter (https://jakevdp.github.io/PythonDataScienceHandbook/) Free online. Covers numpy, pandas, matplotlib, and the data science workflow from first principles. A useful reference throughout Modules 01–05.