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Concept

Hyperplane

Related lectures (30)

Support Vector Machines: SVM Basics

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

Supporting Hyperplanes

Explores supporting hyperplanes for approximating function graphs in higher dimensions through normal vectors and linear approximations.

Introduction to the Perceptron Algorithm

Introduces the perceptron algorithm and its geometric interpretation, emphasizing the rotation of the hyperplane due to misclassified patterns.

Support Vector Machines: Theory and Optimization

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

Differential and Tangent Plan

Explores differentiable functions, gradients, and tangent planes in two-variable functions.

Supporting hyperplanes

Covers supporting hyperplanes for approximating functions' graphs in different dimensions using hyperplanes and linear approximations.

Introduction: classification by a simple perceptron

Covers the concept of a simple perceptron and its ability to solve linearly separable problems by imposing a separating hyperplane.

Linear Models: Recap and Logistic Regression

Covers linear models, binary classification, logistic regression, and model evaluation metrics.

Binary Classification MOOC: IoT Systems and Industrial Applications with Design Thinking

Explains how to find the best hyperplane to separate two classes.

Implicit Function Theorem

Explores the Implicit Function Theorem, supporting hyperplanes, local extrema, and higher-order derivatives, concluding with the classification of stationary points.

Kernel Methods: Efficient Dot Product Computation

Demonstrates efficient dot product computation and introduces nonlinear boundaries in the original space.

Linear Regression: Basics MOOC: IoT Systems and Industrial Applications with Design Thinking

Covers the basics of linear regression, focusing on minimizing errors and predicting outputs.

Implicit Differentiation Examples

Covers examples of implicit differentiation and solving equations involving trigonometric functions.

Assumption of Machine Learning MOOC: IoT Systems and Industrial Applications with Design Thinking

Explores the philosophy of machine learning and the importance of smoothness assumptions in training sets.

Geometry and Least Squares

Discusses the geometry of least squares, exploring row and column perspectives, hyperplanes, projections, residuals, and unique vectors.

Implicit Examples: Hyperplane and Stationary Points

Illustrates finding hyperplanes for surfaces and determining stationary points.

Convex Optimization: Convex Functions Log in to Mediaspace to watch this video

Covers the concept of convex functions and their applications in optimization problems.

Convex Optimization Log in to Mediaspace to watch this video

Covers an overview of convex optimization, affine sets, polyhedra, ellipsoids, and convex functions.

Conjugate Duality: Envelope Representations and Subgradients Log in to Mediaspace to watch this video

Explores envelope representations, subgradients, and the duality gap in convex optimization.

Support Vector Machine: Primal Formulation with Hard Margin Log in to Mediaspace to watch this video

Covers the Support Vector Machine with a hard margin formulation and the importance of maximizing the margin between classes.