Binary Classification

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Related lectures (36)

Support Vector Machines: SVM Basics

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

Support Vector Machines

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

Max-Margin Classifiers

Explores maximizing margins for better classification using support vector machines and the importance of choosing the right parameter.

Linear Models for Classification

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

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: Maximizing Margin

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

SVM for Non-separable Datasets

Explains SVM for non-separable datasets, introducing slack variables and optimizing the margin for classification.

Support Vector Machine Overview

Gives an overview of Support Vector Machines, comparing advantages and disadvantages of SVM with other classifiers.

SVM - Principle: Linear Classifiers

Covers the history and applications of SVM, as well as the construction of linear classifiers and the concept of classifier margin.

Optimization Methods: Theory Discussion

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

Optimization with Constraints: KKT Conditions

Covers the KKT conditions for optimization with constraints, essential for solving constrained optimization problems efficiently.

Optimization Techniques: Convexity in Machine Learning

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

Linear SVM derivation

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

Microeconomic Consumer Theory MOOC: Introduction to Discrete Choice Models

Explores microeconomic consumer theory, utility maximization, optimization problems, and decision-making models.

Linear Programming: Weighted Bipartite Matching

Covers linear programming, weighted bipartite matching, and vertex cover problems in optimization.

Optimization Problems: Greedy Algorithms

Explores optimization problems and greedy algorithms to find the best solutions efficiently.

Contact-Implicit Planning and Control

Introduces state-triggered constraints for non-prehensile manipulation and showcases improved performance through feedback control.

Convex Optimization Tutorial: KKT Conditions Log in to Mediaspace to watch this video

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

Optimal Decision Making: Exercises and Applications Log in to Mediaspace to watch this video

Covers exercises on optimal decision making, including minimizing costs and optimizing transportation networks.

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