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Ali Ghodsi, Lec 1: Principal Component Analysis
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Classroom Contents
Data Visualization and Unsupervised Learning - STAT 841 Winter 2017
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- 1 Ali Ghodsi, Lec 1: Principal Component Analysis
- 2 Ali Ghodsi, Lec 2: PCA (Ordinary, Dual, Kernel)
- 3 Ali Ghodsi, Lec 4: MDS, Isomap, LLE
- 4 Ali Ghodsi, Lec 5: LLE, Spectral Clustering
- 5 Ali Ghodsi, Lec 6: Spectral Clustering, Laplacian Eigenmap, MVU
- 6 Ali Ghodsi, Lec 7: MVU, Action Respecting Embedding, Supervised PCA
- 7 Ali Ghodsi, Lec 8: Supervised PCA
- 8 Ali Ghodsi, Lec 9: SPCA, Nystrom Approximation, NMF
- 9 Ali Ghodsi, Lec 10: NMF via R1D algorithm
- 10 Ali Ghodsi, Lec11: Sum-Product Networks
- 11 Ali Ghodsi, Lec 12: Neural Networks, Autoencoders, Word2Vec
- 12 Ali Ghodsi, Lec 13: Word2Vec Skip-Gram
- 13 Ali Ghodsi, Lec 14: Autoencoders, Clustering, Mixture of Gaussians
- 14 Ali Ghodsi, Lec 15: t-SNE
- 15 Ali Ghodsi, Lec 16: Variational Autoencoders
