Main reference:
- Mohammed J. Zaki, Wagner Meira, Jr., Data mining and machine learning: Fundamental concepts and algorithms, 2nd Edition, Cambridge University Press, March 2020.
Selected topics will be taken from the following textbook:
- Kevin P. Murphy, Machine learning: A probabilistic perspective, MIT press, 2012.
Other topics are covered in articles and monographies, and will be listed here (ongoing):
- Geoffrey Hinton and Sam Roweis, Stochastic Neighbor Embedding, In Advances in Neural Information Processing Systems, vol. 15, pp. 833-840, 2002.
- Laurens Van der Maaten and Geoffrey Hinton, Visualizing data using t-SNE, Journal of Machine Learning Research, vol. 9, no. 11, pp. 2579-2605, 2008.
- Leland McInnes, John Healy and James Melville, UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction, arXiv, 2020.
- Madeleine Udell, Corinne Horn, Reza Zadeh, and Stephen Boyd, Generalized low rank models, Foundations and Trends® in Machine Learning 9, no. 1, pp. 1-118, 2016.
- Madeleine Udell and Alex Townsend, Why are big data matrices approximately low rank? SIAM Journal on Mathematics of Data Science vol. 1, no. 1, pp. 144-160, 2019.