Statistical Machine Learning Part 27 - Multidimensional scaling

Part of the Course "Statistical Machine Learning", Summer Term 2020, Ulrike von Luxburg, University of Tübingen

Join Today
Statistical Machine Learning Part 28 - Random projections and the Theorem of Johnson-Lindenstrauss
▶︎

Statistical Machine Learning Part 28 - Random projections and the Theorem of Johnson-Lindenstrauss

Statistical Machine Learning Part 30 - Isomap
▶︎

Statistical Machine Learning Part 30 - Isomap

16. Learning: Support Vector Machines
▶︎

16. Learning: Support Vector Machines

Statistical Machine Learning Part 19 - The reproducing kernel Hilbert space
▶︎

Statistical Machine Learning Part 19 - The reproducing kernel Hilbert space

Multidimensional Scaling (MDS) | Dimensionality Reduction Techniques (3/5)
▶︎

Multidimensional Scaling (MDS) | Dimensionality Reduction Techniques (3/5)

Introduction to Machine Learning - 11 - Manifold learning and t-SNE
▶︎

Introduction to Machine Learning - 11 - Manifold learning and t-SNE

The Strange Math That Predicts (Almost) Anything
▶︎

The Strange Math That Predicts (Almost) Anything

Ordination using NMDS (Non-metric multidimensional scaling)
▶︎

Ordination using NMDS (Non-metric multidimensional scaling)

ROC and AUC, Clearly Explained!
▶︎

ROC and AUC, Clearly Explained!

Statistical Machine Learning Part 26 - Kernel PCA
▶︎

Statistical Machine Learning Part 26 - Kernel PCA

AlphaFold - The Most Useful Thing AI Has Ever Done
▶︎

AlphaFold - The Most Useful Thing AI Has Ever Done

Support Vector Machines Part 1 (of 3): Main Ideas!!!
▶︎

Support Vector Machines Part 1 (of 3): Main Ideas!!!

8.6 David Thompson (Part 6): Nonlinear Dimensionality Reduction: KPCA
▶︎

8.6 David Thompson (Part 6): Nonlinear Dimensionality Reduction: KPCA

Statistical Machine Learning Part 18 - Kernels: definitions and examples
▶︎

Statistical Machine Learning Part 18 - Kernels: definitions and examples

Statistical Machine Learning Part 35 - Spectral graph theory
▶︎

Statistical Machine Learning Part 35 - Spectral graph theory

But what is the Fourier Transform? A visual introduction.
▶︎

But what is the Fourier Transform? A visual introduction.

Gaussian Processes
▶︎

Gaussian Processes

StatQuest: Principal Component Analysis (PCA), Step-by-Step
▶︎

StatQuest: Principal Component Analysis (PCA), Step-by-Step

Statistical Machine Learning Part 1 - Machine learning and inductive bias
▶︎

Statistical Machine Learning Part 1 - Machine learning and inductive bias

08c Machine Learning: Multidimensional Scaling
▶︎

08c Machine Learning: Multidimensional Scaling