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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.

Lectures

# 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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