Kernel SVM: Polynomial Expansion & Cover's Theorem

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (30)

Page 3 of 3

Support Vector Machine: Model Storage, Memory Usage, and Energy Consumption

Explores SVM model storage, memory usage, training time complexity, and energy consumption estimation.

Lagrangian Duality: Convex Optimization

Explores Lagrangian duality in convex optimization, transforming problems into min-max formulations and discussing the significance of dual solutions.

Kernel Methods: Neural Networks

Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.

Support Vector Machines: Maximizing Margin

Explores Support Vector Machines, maximizing margin for robust classification and the transition to soft SVM for non-linearly separable data.

Implicit Bias in Machine Learning

Explores implicit bias, gradient descent, stability in optimization algorithms, and generalization bounds in machine learning.

Support Vector Machines: Theory and Optimization

Delves into the theory and optimization of Support Vector Machines, including Mercer's Theorem and Lagrange Duality.

Support Vector Machines: SVMs

Explores Support Vector Machines, covering hard-margin, soft-margin, hinge loss, risks comparison, and the quadratic hinge loss.

Convex Optimization Tutorial: KKT Conditions

Explores KKT conditions in convex optimization, covering dual problems, logarithmic constraints, least squares, matrix functions, and suboptimality of covering ellipsoids.

The Hidden Convex Optimization Landscape of Deep Neural Networks

Explores the hidden convex optimization landscape of deep neural networks, showcasing the transition from non-convex to convex models.

Optimal Control: Unconstrained and Constrained Problems

Explores optimal control in unconstrained and constrained problems, emphasizing the importance of sparsity.