References

Books

There are no mandatory readings for this course, except our MLBA website; however, here are some of the books that inspired our course. Except for one book, the rest are all freely available:

  • Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition. Springer Science & Business Media. Available at: Trevor Hastie’s website

  • James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning: with Applications in R. Springer Science & Business Media. Available at: statlearning.com

  • Kuhn, M. and Johnson, K. (2013) Applied Predictive Modeling. Springer Science & Business Media. Available at1: Springer

  • Boehmke, B. and Greenwell, B. (2020) Hands-On Machine Learning with R. Taylor & Francis. Available at: HOML (retrieved the 2023-01-10)

  • Molnar C. (2023) Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. Available at: Interpretable Machine Learning (retrieved the 2023-08-10)

License

This course is provided under Creative Commons License which is the Creative Commons Attribution-ShareAlike 4.0 International license.

Acknowledgements

The design of the MLBA course website was adapted and inspired from the following sources:

Footnotes

  1. This is the only book that is not freely available and must be purchased.↩︎