1. Guidance, Introduction to machine learning
2. Principal components analysis (PCA) and SVD
3. Probabilistic PCA and Factor analysis
4.k-means clustering and Gaussian mixture model
5. Kernel PCA and Manifold learning
6. Laplacian eigenmap and Spectral clustering
7. Non-negative matrix factorization
8. Sparse coding and Dictionary learning
9. Kernel densitiy estimation
10. Variational auto-encoders and Generative adversarial networks
11. Dynamic mode decomposition
12. Anomaly detection based on Unsupervised learning
13. Review (Presentation)