Concept

Linear separability

Related lectures (31)

Covers the concept of kernel regression and making data linearly separable by adding features and using local methods.

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

Covers the Perceptron algorithm and its application to binary classification problems, including the Pocket Perceptron algorithm.

Support Vector Machines: Definition and Separation Hyperplane

Covers the history, linear separability, hyperplanes, and support vectors in Support Vector Machines.

Deep Learning Paradigm

Explores the deep learning paradigm, including challenges, neural networks, robustness, fairness, interpretability, and energy efficiency.

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: Linear Separability

Explores linear separability in support vector machines, focusing on hyperplane separation and margin optimization.

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.

Redescription SVM and Hilbert Spaces

Explores redescription SVM and Hilbert spaces for solving nonlinear problems using inner product spaces and separable Hilbert spaces.

Support Vector Machines

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

Deep Learning: Theory and Applications

Explores the mathematics of deep learning, neural networks, and their applications in computer vision tasks, addressing challenges and the need for robustness.

Machine Learning Fundamentals

Introduces the basics of machine learning, covering supervised classification, logistic regression, and maximizing the margin.

Perception: Image Classification Challenges

Covers image classification challenges, machine learning concepts, linear regression, and nearest neighbor approach in autonomous vehicles.

Introduction to Supervised Learning: Classification and Perceptrons

Explores supervised learning through classification as a geometric problem and the concept of finding a separating surface.

Kernel Regression: Weighted Average and Feature Maps

Covers kernel regression and feature maps for data separability.

XOR Problem: Neural Networks

Delves into solving the XOR problem using a two-layer neural network.

PCA and Kernel PCA

Explains how PCA eliminates dimensions by finding principal components with most variation and compares PCA with Kernel PCA.

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.

Kernel Regression: K-nearest Neighbors

Covers the concept of kernel regression and K-nearest neighbors for making data linearly separable.

Introduction to the Perceptron Algorithm

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