1. Linear, logistic and multinomial regularized regression (L1, L2, ElasticNet)
2. stochastic gradient descent, FISTA, coordinate descent
3. cross validation
4. Basic sparse matrix operations
5. PCA, SVD
6. k-means, k-medoid

Most of these work on both sparse and dense matrices.

MIT, 2016 Istvan Bartha