CS233 Class Schedule for Spring Quarter '15-'16



March 29
March 31

Introduction; Geometric and topological perspective on data analysis; Data representations: point clouds and graphs; Joint data analysis.


Lecture 1 Slides

Example visual datasets: ImageNet and ShapeNet.

Reading: ImageNet, 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. Non-linear 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, persistence-based segmentation, scalar fields, ToMATo

Lecture 9 Slides

Overview of optimization methods: convex and non-convex 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. Bag-of-words models.

Reading: Chapter 4 of the Szeliski book; the Harris paper; the SIFT paper; bag-of-words 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

Non-rigid 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 multi-class 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.




No class.


Homework 4 due.