Session III: Principal Component Analysis & Clustering Methods

Topics

• Dimensionality Reduction

• Principal Component Analysis (PCA)

• Clustering: K-Means & Lloyd’s Algorithm

PDF Notes

Session3_Slides.pdf
Session3_Slides_PCA.pdf

Jupyter Notebooks

session3a_K-means.ipynb
session3a_K-means_advanced.ipynb
session3b_PCA.ipynb
session3b_PCA_advanced.ipynb

References

Principal Component Analysis

• Chapter 10.2 of James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. “An Introduction to Statistical Learning”, Vol. 112. Springer, 2013 (PDF available).
• Chapter 10 of Marc P. Deisenroth and A. Aldo Faisal and Cheng Soon Ong, “Mathematics for Machine Learning”, Cambridge University Press, 2020 (PDF available).
• Genetics application: Paschou, P., Ziv, E., Burchard, E. G., Choudhry, S., Rodriguez-Cintron, W., Mahoney, M. W., & Drineas, P., “PCA-correlated SNPs for structure identification in worldwide human populations”, PLoS Genet, 3(9), e160.

Clustering: K-Means and Spectal

• Chapter 21 of Kevin P. Murphy, “Probabilistic Machine Learning: An introduction”, MIT Press, 2021 (PDF available).
• Chapter 22 of Shai Shalev-Shwartz and Shai Ben-David. “Understanding machine learning: From theory to algorithms”, Cambridge University Press, 2014 (PDF available).
• Chapter 14 of Trevor Hastie, Robert Tibshirani, and Jerome Friedman, “The Elements of Statistical Learning: Data Mining, Inference, and Prediction”, Springer, 2009 (PDF available).