Linear separability

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Related lectures (31)

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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.