Kernel SVM: Polynomial Expansion & Cover's Theorem

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Convexifying Nonconvex Problems: SVM and Dimensionality Reduction

Explores convexifying nonconvex problems through SVM and dimensionality reduction techniques.

Linear Models for Classification

Explores linear models, logistic regression, classification metrics, SVM, and their practical use in data science methods.

Linear Models for Classification

Covers linear models for classification, including SVM, decision boundaries, support vectors, and Lagrange duality.

Support Vector Machines: SVM Basics

Covers the basics of Support Vector Machines, focusing on hard-margin and soft-margin formulations.

Differentiable Functions and Lagrange Multipliers

Covers differentiable functions, extreme points, and the Lagrange multiplier method for optimization.

Optimization Techniques: Stochastic Gradient Descent and Beyond

Discusses optimization techniques in machine learning, focusing on stochastic gradient descent and its applications in constrained and non-convex problems.

Equivalent formulation

Covers the concept of equivalent formulation in constrained optimization and explores the tangent cone.

Convexifying Nonconvex Problems: MGT-418 Lecture

Explores convexifying nonconvex problems through kernel tricks, sensitivity interpretation, and nonlinear dimensionality reduction.

Optimization Techniques: Convexity in Machine Learning

Covers optimization techniques in machine learning, focusing on convexity and its implications for efficient problem-solving.

Image Denoising and Reconstruction

Covers denoising and reconstructing images using total variation minimization and discusses the visual effects of different regularization strengths.