01:26:04

Shape Analysis, spring 2023 (lecture 3): Smooth curves

01:13:38

Lecture 5: Smooth and discrete surfaces (warning: camera broke!)

01:21:47

Shape Analysis, spring 2023 (lecture 21): More optimal transport

01:23:12

Shape Analysis, spring 2023 (lecture 7): Discrete curvature

01:19:12

Shape Analysis, spring 2023 (lecture 11): Structure-preserving embedding

01:21:25

Shape Analysis, spring 2023 (lecture 22): Shape correspondence

01:22:58

Shape Analysis, spring 2023 (lecture 23): Consistent correspondence I (camera broke)

01:12:47

Shape Analysis, spring 2023 (lecture 19): Polyvectors, discrete vector fields

01:18:20

Shape Analysis, spring 2023 (lecture 18): Vector field theory

01:22:21

Shape Analysis, spring 2023 (lecture 15): Low-dimensional Applications of the Laplacian

55:44

Shape Analysis, spring 2023 (lecture 24): Consistent Correspondence II

01:03:03

Shape Analysis, spring 2023 (lecture 6b): Curvature of surfaces

01:22:23

Shape Analysis, spring 2023 (lecture 4): Discrete curves

01:15:25

Shape Analysis, spring 2023 (lecture 20): Continuous normalizing flows, Intro to optimal transport

01:08:40

Shape Analysis, spring 2023 (lecture 1): Introduction

01:22:12

Shape Analysis, spring 2023 (lecture 8): Geodesic distance algorithms

01:16:22

Shape Analysis, spring 2023 (lecture 7): Geodesic distances

01:17:55

Shape Analysis, spring 2023 (lecture 2): Linear and Variational Problems

01:25:11

Shape Analysis, spring 2023 (lecture 13): Laplacians II

01:23:50

Shape Analysis, spring 2023 (lecture 14): Discretizing the Laplacian

17:36

Shape Analysis, spring 2023 (lecture 6a): Data structures for meshes

01:23:16

Shape Analysis, spring 2023 (lecture 17): Manifold optimization (mic broke...)

01:13:04

Shape Analysis, spring 2023 (lecture 16): Laplacians on point clouds, ML applications

01:22:16

Shape Analysis, spring 2023 (lecture 10): Euclidean embedding

01:21:17

Shape Analysis, spring 2023 (lecture 12): Laplacians I

01:21:38

Applied Numerical Algorithms, fall 2023 (lecture 25): Leapfrog, adjoint method, neural ODE

01:24:02

Applied Numerical Algorithms, fall 2023 (lecture 24): Trapezoid/exponential/Newmark integration

01:22:43

Applied Numerical Algorithms, fall 2023 (lecture 23): Numerical ODE, simple integrators

01:24:00

Applied Numerical Algorithms, fall 2023 (lecture 22): More quadrature, numerical differentiation

01:19:17

Applied Numerical Algorithms, fall 2023 (lecture 21): Quadrature in 1D

01:20:30

Applied Numerical Algorithms, fall 2023 (lecture 20): Interpolation

01:20:24

Applied Numerical Algorithms, fall 2023 (lecture 19): Alternation, ADMM, proximal methods

01:19:32

Applied Numerical Algorithms, fall 2023 (lecture 18): Nonlinear least-squares, alternation

01:21:44

Applied Numerical Algorithms, fall 2023 (lecture 17): More conjugate gradients; preconditioning

01:22:31

Applied Numerical Algorithms, fall 2023 (lecture 16): Constraints; intro to conjugate gradients

01:19:21

Applied Numerical Algorithms, fall 2023 (lecture 15): Constrained optimization, KKT conditions

01:20:01

Applied Numerical Algorithms, fall 2023 (lecture 14): BFGS, DFP, Quasi-Newton optimization

01:21:39

Applied Numerical Algorithms, fall 2023 (lecture 13): Gradient descent, line search

01:15:10

Applied Numerical Algorithms, fall 2023 (lecture 12): Broyden low-rank updates, 1D optimization

01:20:32

Applied Numerical Algorithms, fall 2023 (lecture 11): Root-finding, Newton's/Broyden's methods

01:17:13

Applied Numerical Algorithms, fall 2023 (lecture 10): Applications of SVD; Procrustes problem

01:23:17

Applied Numerical Algorithms, fall 2023 (lecture 9): QR iteration, SVD

01:20:53

Applied Numerical Algorithms, fall 2023 (lecture 8): Eigenvalue iteration, deflation

01:08:30

Applied Numerical Algorithms, fall 2023 (lecture 7): Applications of eigenvectors

01:18:46

Applied Numerical Algorithms, fall 2023 (lecture 6): QR factorization

59:16

Applied Numerical Algorithms, fall 2023 (lecture 5): Condition number for linear systems

01:20:24

Applied Numerical Algorithms, fall 2023 (lecture 4): Cholesky factorization, sparse matrices

01:19:45

Applied Numerical Algorithms, fall 2023 (lecture 3): LU factorization, designing linear systems

01:21:44

Applied Numerical Algorithms, fall 2023 (lecture 2): Conditioning, linear systems, Gaussian elim.

01:21:06

Applied Numerical Algorithms, fall 2023 (lecture 1): Introduction, number systems, measuring error

06:03

Pachelbel's Canon (cello quartet arr. Malte Meyn)

01:27:50

Shape Analysis (Lecture 2): Linear and variational problems

23:20

Shape Analysis (Lectures 14, extra content): A simple Laplacian on point clouds

01:26:08

Shape Analysis (Lecture 3): Differential geometry of smooth curves

30:53

Shape Analysis (Lectures 21, extra content): Reversible harmonic maps between discrete surfaces

01:18:31

Shape Analysis (Lecture 10, part 2): Algorithms for embedding into Euclidean space

01:16:12

Shape Analysis (Lecture 8): Geodesic distances

30:18

Shape Analysis (Lectures 18, extra content): Manifold optimization for PCA problems

43:30

Shape Analysis (Lecture 10): Metric spaces and embeddings

01:35:06

Shape Analysis (Lecture 5): Defining surfaces and (sub)manifolds; mesh data structures