CS233 Class Schedule for Spring Quarter '17'18
Monday

Wednesday

April 2

April 4

Introduction; Geometric and topological perspective on data analysis; Data representations; Learning on point clouds and graphs; Joint data analysis. Reading: Lecture 1 Slides 
Visual data sets: ImageNet and ShapeNet; Techniques for annotation and annotation transport. Reading: ImageNet, ShapeNet, Annotation1, Annotation2 Lecture 2 Slides 
April 9

April 11

Linear algebraic techniques: principal components analysis (PCA), Kernel PCA. Reading: PCA Tutorial, KPCA Lecture 3 Slides 
Linear algebraic techniques: canonical correlation analysis (CCA). Reading: CCA Tutorial, CCA2 Homework 1 out. Lecture 4 Slides 
April 16

April 18

Graph methods; spectral approaches, graph Laplacians, Laplacian embeddings, spectral clustering. Reading: Spectral graph theory Yale course (first few lectures); spectral clustering tutorial Lecture 5 Slides 
Multidimensional scaling. Nonlinear dimensionality reduction: locally linear embeddings, Laplacian eignemaps, Isomap, tSNE. Reading: MDS1, MDS2, Isomap, LE, LLE, tSNE Lecture 6 Slides 
April 23

April 25

Computational topology: topology review, complexes, homology groups. Reading: Topology and Data Lecture 7 Slides 
Persistent homology, barcodes and persistence diagrams. Reading: Barcodes, Persistent Homology Homework 1 due. Homework 2 out. Lecture 8 Slides 
April 30

May 2

Topological inference; the Mapper algorithm. Applications. Reading: Shape barcodes, Mapper, persistencebased segmentation, scalar fields, ToMATo Lecture 9 Slides 
Representations of 3D Geometry: VoxelGrids, Point Clouds, Meshes and Other Boundary Models, Solid Models Reading: Old survey Lecture 10 Slides 
May 7

May 9

Geometry processing; LaplaceBeltrami and other operators on meshes; Lecture 11 Slides 
Shape alignment. Global and local shape descriptors; intrinsic descriptors, heat and wave kernel signatures. Reading: ICP; RANSAC; Shape descriptors for retrieval; global point signatures; heat kernel signatures; Homework 2 due. Homework 3 out. Lecture 12 Slides 
May 14

May 16

Nonrigid shape alignment; isometric matching, conformal maps, Möbius voting, blended intrinsic maps Reading: one point isometric matching; Möbius maps; Möbius voting; blended intrinsic maps Lecture 13 Slides 
Deep learning; Volumetric and multiview CNNs for 3D geometry Reading: MVCNN1, MCCNN2, VoxelCNN1, VoxelCNN2 Lecture 14 Slides 
May 21

May 23

Deep nets for pointclouds Reading: PointNet, PointNet++ Lecture 15 Slides 
Deep nets for graphs and meshes Reading: spectral, graph, geodesic, sync, survey Homework 3 due. Homework 4 out. Lecture 16 Slides 
May 28

May 30

Memorial day holiday  no class 
Functional spaces and functional maps, variations; map visualization Reading: functional maps paper; map visualization; Siggraph 17 course notes Lecture 17 Slides 
June 4

June 6

Shape differences and shape variability. Reading: shape differences, cosegmentation Lecture 18 Slides 
Networks of shapes and images; cycle consistency; map processing and latent spaces. Reading: multilatent space cosegmentation, 3D object cosegmentation Lecture 19 Slides Homework 4 due. 