Spring 2026 • Northeastern University

Bibliography & Acknowledgments

While all of the content on this site is my own work, I drew significant inspiration from several excellent resources in the field of machine learning and statistics.

Primary References

ISLP

An Introduction to Statistical Learning with Applications in Python

James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). An Introduction to Statistical Learning with Applications in Python. Springer.

This accessible textbook provides a broad and less technical treatment of key topics in statistical learning, making it an accessible entry point or refresher for students with varied statistical and programming backgrounds.

PML Books 1 and 2

Probabilistic Machine Learning: An Introduction

Murphy, K. P. (2022). Probabilistic Machine Learning: An Introduction. MIT Press.

Probabilistic Machine Learning: Advanced Topics

Murphy, K. P. (2023). Probabilistic Machine Learning: Advanced Topics. MIT Press.

Kevin Murphy's comprehensive two-volume series provides an outstanding foundation for probabilistic machine learning, covering both introductory and advanced topics with mathematical rigor and clarity. I encourage my students to download and/or purchase these books as theoretical references that they can leverage beyond this course.

Probabilistic Machine Learning Course (Tübingen)

Probabilistic Machine Learning — Lecture Course

Hennig, P. (2025). Probabilistic Machine Learning. Lecture course, University of Tübingen.

This excellent lecture course from the Tübingen International Master Programme on Machine Learning provides a thorough introduction to probabilistic machine learning concepts. The first few lectures in particular complement PML Book 1's introductions of probability theory and Bayesian inference, and inspired my approach to introducing these topics in this course.

Acknowledgments

I would like to express my gratitude to my former professors and current colleagues, who provided invaluable guidance, advice, and shared materials that helped me prepare this course:

Citation

If you use materials from this site, please cite:

@misc{mathieu2026cs6140,
  author = {Mathieu, Philip E.},
  title = {CS 6140: Machine Learning Interactive Demos},
  year = {2026},
  publisher = {Northeastern University},
  url = {https://github.com/PEM-Instruct/cs6140-apps}
}