Concept
Margin (machine learning)
Related lectures (24)
Support Vector Machine: Primal Formulation with Hard Margin
Covers the Support Vector Machine with a hard margin formulation and the importance of maximizing the margin between classes.
Linear SVM derivation
Covers the derivation of Linear Support Vector Machine (SVM) and the Karush-Kuhn-Tucker (KKT) conditions.
Machine Learning Fundamentals
Introduces the basics of machine learning, covering supervised classification, logistic regression, and maximizing the margin.
Feature Expansion and Kernels
Covers feature expansion, kernels, SVM, and nonlinear classification in machine learning.
Support Vector Machine and Logistic Regression
Explains support vector machine and logistic regression for classification tasks, emphasizing margin maximization and risk minimization.
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.
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.
Support Vector Machines: Definition and Separation Hyperplane
Covers the history, linear separability, hyperplanes, and support vectors in Support Vector Machines.
Farkas' Lemma: Applications in Game Theory
Explores Farkas' Lemma, hyperplane separation, combinatorics, and its application in game theory, focusing on penalty kick strategies.
Deep Learning: Theory and Practice
By Prof. Volkan Cevher delves into the mathematics of deep learning, exploring model complexity, risk trade-offs, and the generalization mystery.