Concept

Hinge loss

Related lectures (30)

Introduces Support Vector Machines, covering Hinge Loss, hyperplane separation, and non-linear classification using kernels.

Support Vector Machines: Basics and Applications

Covers the basics of support vector machines, logistic regression, decision boundaries, and the k-Nearest Neighbors algorithm.

Maximum Likelihood Estimation: Linear Models

Explores maximum likelihood estimation in linear models, covering Gaussian noise, covariance estimation, and support vector machines for classification problems.

Linear Models for Classification

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

Linear Models & k-NN

Covers linear models, logistic regression, decision boundaries, k-NN, and practical applications in authorship attribution and image data analysis.

Robust Optimization: Radiation Therapy & Support Vector Machines

Explores robust optimization in radiation therapy and support vector machines.

Linear Models for Classification

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

Support Vector Machines: SVMs

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

Support Vector Machines: Soft Margin

Explores Support Vector Machines with a focus on soft margin and multiclass classification using binary classifiers.

Linear Models: Classification Basics

Explores linear models for classification, logistic regression, SVM, k-NN, and curse of dimensionality.

Classification Problems: Overview and Loss Functions

Covers classification problems and various loss functions used in machine learning.

Generalized Linear Regression

Explores generalized linear regression, logistic regression, and multiclass classification in machine learning.

Multiclass Classification

Covers the concept of multiclass classification and the challenges of linearly separating data with multiple classes.

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

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

SVMs and Feature Maps

Explores SVMs, feature maps, and the importance of finding the maximum margin solution for classification problems.

Bayesian Inference: Part 2

Explores Bayesian inference, multiclass classification, logistic regression, and linear regression inference.

Linear Models for Classification: Logistic Regression and SVM

Covers linear models for classification, focusing on logistic regression and support vector machines.

SVM and Multiclass Classification

Covers SVM and multiclass classification using one-vs-all and one-vs-one approaches.

Linear SVM derivation

Covers the derivation of Linear Support Vector Machine (SVM) and the Karush-Kuhn-Tucker (KKT) conditions.

Feature Expansion and Kernels

Covers feature expansion, kernels, SVM, and nonlinear classification in machine learning.