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DSCI 563: Unsupervised Learning

How to find groups and other structure in unlabeled, possibly high dimensional data. Dimension reduction for visualization and data analysis. Clustering and model fitting via the EM algorithm.


# Date Day Topic Slides
1 2019-01-03 Thur Unsupervised paradigm; Clustering: K-Means and FCM; Choosing K; lecture 1
2 2019-01-08 Tue K-Medians; K-Medoids; Hierarchical Clustering; DBSCAN lecture 2
3 2019-01-10 Thur EM algorithm and Gaussian Mixtures lecture 3
4 2019-01-15 Tue Principal Component Analysis (Mike's class) lecture 4
5 2019-01-17 Thur NMF, Sparse PCA (Mike's class) lecture 5
6 2019-01-22 Tue Recommender Systems (Mike's class) lecture 6
7 2019-01-24 Thur More on Recommender Systems and Multidimensional Scaling (Mike's class)
8 2019-01-29 Tue GAP statistic, FCM, t-SNE, Isomap, eigenfaces

Reference Material and other Resources

  • [JWHT13]: James, G., Witten, D., Hastie, T. and Tibshirani, R. An Introduction to Statistical Learning. 2013. Springer-Verlag New York

  • [HTF09]: Hastie, T., Tibshirani, R. and Friedman, J. The Elements of Statistical Learning. 2009. Second Edition. Springer-Verlag New York

Linear algebra review

There are a bunch of suggestions here. We particularly recommend essence of linear algebra (YouTube series) and Immersive linear algebra (interactive e-book).

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