Matrix Completion Courses and Certifications

Georgia Tech Research

Inference and Uncertainty Quantification for Noisy Matrix Completion - Lecture 5

Explore inference and uncertainty quantification for noisy matrix completion, focusing on de-biased estimators and their distributional characterizations for optimal confidence interval construction.

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

Simons Institute

Can Non-Convex Optimization Be Robust?

Explore non-convex optimization's robustness, examining locally optimizable functions, perturbed objectives, and matrix completion. Insights on empirical vs. population risk and semi-random adversaries.

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

Institute for Pure & Applied Mathematics (IPAM)

Nonconvex Optimization in Matrix Optimization and Distributionally Robust Optimization

Explore nonconvex optimization in matrix and distributionally robust contexts, covering algorithms, complexity, and applications in machine learning with Stephen Wright.

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

Simons Institute

Finding Low-Rank Matrices - From Matrix Completion to Recent Trends

Explore low-rank matrix recovery, from matrix completion to recent trends, covering theoretical foundations and applications in signal processing and machine learning.

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

Hausdorff Center for Mathematics

The Convex Geometry of Blind Deconvolution and Matrix Completion Revisited

Explores convex geometry in blind deconvolution and matrix completion, analyzing dimensional factors in noise bounds and proposing alternative error scaling approaches for low-rank matrix recovery problems.

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

UofU Data Science

Matrix Completion and PageRank in Data Mining - Spring 2023

Dive into matrix completion techniques and PageRank algorithms, exploring fundamental concepts for data mining and graph analysis applications.

• YouTube
• 1 hour 20 minutes
• On-Demand
• Free Video

Learning Time Series with Interventions

24 ratings at edX

An in-depth introduction to time series analysis, from learning structured models to predictions and reinforcement learning, with hands-on projects - Part of the MITx MicroMasters program in Statistics and Data Science.

• edX
• 14 weeks, 10-14 hours a week
• On-Demand
• Free Online Course (Audit)

International Centre for Theoretical Sciences

Causal Matrix Completion - Applications to Offline

Explore causal matrix completion and its applications in offline settings, focusing on probabilistic and optimization methods in data science.

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

Fields Institute

Robust Low-Rank Matrix Completion via an Alternating Manifold Proximal Gradient Continuation Method

Explore robust low-rank matrix completion using an alternating manifold proximal gradient method. Learn about its applications in computer vision, signal processing, and machine learning.

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

Institute for Advanced Study

Is Optimization the Right Language to Understand Deep Learning? - Sanjeev Arora

Explore deep learning optimization, generalization, and neural tangent kernels with Sanjeev Arora. Gain insights into training wide networks and matrix completion techniques.

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

Fields Institute

Geometric Constraint Systems

Explore rigidity theory, tropical geometry, matrix completion, and graphical models through comprehensive lectures on geometric constraint systems and their applications in data science.

• YouTube
• 20 hours 48 minutes
• On-Demand
• Free Video

Paul G. Allen School

Better Understanding of Non-convex Methods in Machine Learning

Exploring non-convex optimization in machine learning, focusing on matrix completion and neural network architectures. Insights into algorithmic approaches for complex, high-dimensional models.

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

Simons Institute

Analyzing Optimization and Generalization in Deep Learning via Dynamics of Gradient Descent

Explore deep learning optimization and generalization through gradient descent dynamics, focusing on implicit preconditioning and its impact on matrix completion problems.

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

Simons Institute

Implicit Regularization I

Explore implicit regularization in deep learning, covering boosting, complexity control, optimization landscapes, and stochastic gradient descent for effective model training.

• YouTube
• 1 hour 17 minutes
• On-Demand
• Free Video

Simons Institute

Power of Active Sampling for Unsupervised Learning

Explore active sampling techniques for unsupervised learning, focusing on matrix completion, graphical models, and clustering. Learn about statistical tradeoffs and computational complexity.

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