Tue/Thu: 1045-1205. WEB L114
- Convex Hulls I: Convexity, representation of convex hulls, the gift-wrapping algorithm
- Convex Hulls II: Andrew's algorithm, Chan's optimal algorithm, higher dimensions and the data connection.
- Arrangements and Duality: Arrangements, Duality and the Zone Theorem.
- Voronoi Diagrams I
- Voronoi Diagrams II
- Delaunay Triangulations (starting at page 74)
- Data Structures
- quad-trees/range trees/kd-trees
- Polyhedra and Optimization
- Sampling and Learning (Chapter 5 from this book)
- VC dimension
- epsilon-nets and epsilon-samples
- discrepancy-based constructions
- High Dimensional Geometry
- SVDs, PCA and MDS
- Random projections (and an application to clustering)
- Near neighbors (see these nice Euclidean LSH notes, and this for the main LSH resource page)
- Core sets and extents (see Chapter 23 from this book)
- The Geometry of Learning
- Kernels and Hilbert spaces
- Support Vector Machines
- The graph Laplacian
- Bregman Divergences.
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