CS233 Class Schedule for Spring Quarter '15'16
Tuesday

Thursday

March 29

March 31

Introduction; Geometric and topological perspective on data analysis; Data representations: point clouds and graphs; Joint data analysis. Reading: Lecture 1 Slides 
Example visual datasets: ImageNet and ShapeNet. Lecture 2 Slides 
April 5

April 7

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 Homework 1 out. Lecture 4 Slides 
April 12

April 14

Graph methods; spectral approaches, graph Laplacians, Laplacian embeddings, spectral clustering. Reading: Spectral graph theory Yale course; spectral clustering tutorial Lecture 5 Slides 
Multidimensional scaling. Nonlinear dimensionality reduction: locally linear embeddings, Laplacian eignemaps, Isomap. Reading: MDS1, MDS2, Isomap, LE, LLE Lecture 6 Slides 
April 19

April 21

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 26

April 28

Topological inference; the Mapper algorithm. Applications. Reading: Shape barcodes, Mapper, persistencebased segmentation, scalar fields, ToMATo Lecture 9 Slides 
Overview of optimization methods: convex and nonconvex optimization. Reading: https://stanford.edu/~boyd/papers/ cvx_short_course.html, http://ee364a.stanford.edu Lecture 10 Slides 
May 3

May 5

Classical image descriptors, Harris corners, SIFT. Bagofwords models. Reading: Chapter 4 of the Szeliski book; the Harris paper; the SIFT paper; bagofwords survey. Lecture 11 Slides 
Global and local shape descriptors; intrinsic descriptors, heat and wave kernel signatures. Reading: Shape descriptors for retrieval; heat kernel signatures; ShapeGoogle Homework 2 due. Homework 3 out. Lecture 12 Slides 
May 10

May 12

Learned descriptors: convolutional nets. Reading: Andrej notes; convnets demo; Goodfellow/Bengio book chapter on convnets Lecture 13 Slides 
Rigid shape alignment: ICP, distance function fields, RANSAC, geometric hashing Reading: ICP; distance function fields; RANSAC; geometric hashing Lecture 14 Slides 
May 17

May 19

Nonrigid alignment, isometric matching, conformal maps, Möbius voting, blended intrinsic maps Reading: one point isometric matching; global point signatures; Möbius voting; blended intrinsic maps Lecture 15 Slides 
Functional spaces and functional maps, variations; map visualization Reading: functional maps paper; functional maps notes; map visualization Homework 3 due. Homework 4 out. Lecture 16 Slides 
May 24

May 26

Networks of shapes and images; cycle consistency; map processing and latent spaces. Reading: image shared structure; image multiclass structure; shape shared structure Lecture 17 Slides 
Primal cycle consistency. Shape differences and shape variability. Reading: primal cyclce consistency; SDP; shape differences Lecture 18 Slides 
May 31

June 2

Course summary. Reading:

No class.
Homework 4 due. 