Manifold Learning Courses and Certifications

Institut des Hautes Etudes Scientifiques (IHES)

Manifold Learning with Noisy Data: Dimension Reduction and Support Estimation

Explore manifold learning techniques for high-dimensional data with noise, covering dimension reduction, support estimation, and robustness in geometric approaches.

• YouTube
• 43 minutes
• On-Demand
• Free Video

Institute for Pure & Applied Mathematics (IPAM)

Open Mathematical Problems in Manifold Learning for Single-Cell Data

Explore open mathematical challenges in manifold learning for single-cell cancer data, focusing on t-SNE and UMAP limitations in structure preservation and stability.

• YouTube
• 51 minutes
• On-Demand
• Free Video

Computational Genomics Summer Institute CGSI

Harnessing Data Geometry for Biomedical Insights

Explore data geometry techniques for biomedical insights, covering manifolds, diffusion operators, and spectral clustering in stem cell development and density estimation.

• YouTube
• 49 minutes
• On-Demand
• Free Video

Erwin Schrödinger International Institute for Mathematics and Physics (ESI)

Learning Manifolds and Dimensionality Reduction in Deep and Geometric Learning

Explore geometric principles and manifold structures in data spaces, examining dimensionality reduction techniques for deep learning applications through mathematical and physical perspectives.

• YouTube
• 52 minutes
• On-Demand
• Free Video

Institute for Pure & Applied Mathematics (IPAM)

Score-Based Generative Models through the Lens of Wasserstein Proximal Operators

Explore score-based generative models as Wasserstein proximal operators through mean-field games, featuring kernel-based approaches for enhanced training efficiency and manifold learning.

• YouTube
• 27 minutes
• On-Demand
• Free Video

HUJI Machine Learning Club

Manifold Denoising with Application to Electron Microscopy

Discover advanced manifold denoising techniques for electron microscopy image processing, focusing on Fourier-like basis construction and filtering methods to enhance two-dimensional image collections through mathematical analysis.

• YouTube
• 56 minutes
• On-Demand
• Free Video

Institute for Pure & Applied Mathematics (IPAM)

Inferring Dynamic Cellular Trajectories and Underlying Cellular Regulatory Networks with Neural and Graph ODE Models - Part 2

Explore graph ODE frameworks for inferring regulatory mechanisms in dynamic cellular trajectories, featuring RITINI method and breast cancer case studies.

• YouTube
• 30 minutes
• On-Demand
• Free Video

Institute for Pure & Applied Mathematics (IPAM)

Inferring Dynamic Cellular Trajectories and Underlying Cellular Regulatory Networks with Neural and Graph ODE Models - Part 1/2

Explore PHATE embedding and MIOflow neural ODE methods for analyzing cellular data trajectories and discovering regulatory networks in cancer research applications.

• YouTube
• 39 minutes
• On-Demand
• Free Video

Joint Mathematics Meetings

Mathematical Frameworks for Signal and Image Analysis - Diffusion Methods in Manifold and Fibre Bundle Learning

Explore advanced mathematical techniques for signal and image analysis, focusing on diffusion methods in manifold and fiber bundle learning with renowned mathematician Ingrid Daubechies.

• YouTube
• 53 minutes
• On-Demand
• Conference Talk

BIMSA

Learning Manifold-Structured Data Using Deep Neural Networks - Theory and Applications

Explore deep neural networks' ability to learn geometric data structures using multi-chart latent spaces and Chart Auto-Encoders, with theoretical foundations and practical applications.

• YouTube
• 1 hour 6 minutes
• On-Demand
• Free Video

IEEE Signal Processing Society

Low Distortion Embeddings With Bottom-up Manifold Learning

Explore bottom-up manifold learning techniques for creating low distortion embeddings, enhancing data visualization and dimensionality reduction in signal processing applications.

• YouTube
• 57 minutes
• On-Demand
• Free Video

Harvard CMSA

Extrinsic and Intrinsic Manifold Learning - Old and New

Explore extrinsic and intrinsic manifold learning techniques, from testing manifold hypotheses in high-dimensional spaces to handling noisy distance data without embeddings.

• YouTube
• 49 minutes
• On-Demand
• Free Video

Applied Unsupervised Learning in Python

Master unsupervised learning algorithms in Python to uncover hidden patterns, reduce dimensions, cluster data, model topics, and handle missing values using real-world datasets.

• Coursera
• 1 day 6 hours 13 minutes
• On-Demand
• Paid Course

Yannic Kilcher

The Dimpled Manifold Model of Adversarial Examples in Machine Learning - Research Paper Explained

Explores the Dimpled Manifold Model to explain adversarial examples in machine learning, challenging existing theories and providing experimental evidence for a new perspective on neural network vulnerabilities.

• YouTube
• 1 hour 14 minutes
• On-Demand
• Free Video

Inside Livermore Lab

Guided Deep Learning Manifold Linearization of Porous Media Flow Equations for Digital Twins Operations

Explore advanced techniques in Digital Twin operations for reservoir management, focusing on physics-aware machine learning methods to enhance simulation accuracy and efficiency.

• YouTube
• 56 minutes
• On-Demand
• Free Video