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Applied topology 1: Datasets have shape
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Applied Topology - Topological Data Analysis 2021
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- 1 Applied topology 1: Datasets have shape
- 2 Applied topology 2: Topology and homotopy equivalences
- 3 Applied topology 3: A punctured torus is homotopy equivalent to a figure eight
- 4 Applied topology 4: An introduction to the torus and Klein bottle
- 5 Applied topology 5: Spheres in all dimensions
- 6 Applied topology 6: Homology
- 7 Applied topology 7: How do you recover the shape of a dataset?
- 8 Applied topology 8: An introduction to persistent homology
- 9 Applied topology 9: Spaces of 3x3 natural image patches
- 10 Applied topology 10: Unsupervised vs supervised learning
- 11 Applied topology 11: Clustering and K-means clustering
- 12 Applied topology 12: Hierarchical clustering and single-linkage clustering
- 13 Applied topology 13: The problem of chaining in single-linkage clustering
- 14 Applied topology 14: Cech and Vietoris-Rips simplicial complexes
- 15 Applied topology 15: Introduction to a software tutorial for persistent homology and Ripser
- 16 Applied topology 16: Sublevelset persistent homology
- 17 Applied topology 17: Persistence and local geometry, Part A
- 18 Applied topology 18: Persistence and local geometry, Part B
- 19 Applied topology 19: Linear dimensionality reduction - Principal Component Analysis (PCA), Part I
- 20 Applied topology 20: Linear dimensionality reduction - Principal Component Analysis (PCA), Part II
- 21 Applied topology 21: Nonlinear dimensionality reduction - Isomap, Part I
- 22 Applied topology 22: Nonlinear dimensionality reduction - Isomap, Part II
- 23 Applied topology 23: Paper Introduction: Coordinate-free coverage in sensor networks
- 24 Applied topology 24: Evasion paths in mobile sensor networks, Part I
- 25 Applied topology 25: Evasion paths in mobile sensor networks, Part II
- 26 Applied topology 26: Evasion paths in mobile sensor networks, Part III
- 27 Applied topology 27: Evasion paths in mobile sensor networks, Part IV
