Support Vector Machines: Linear Separability

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Covers the perceptron model and backpropagation algorithm in neural networks.

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

Explains support vector machine and logistic regression for classification tasks, emphasizing margin maximization and risk minimization.

Covers fundamental machine learning concepts including Structure Discovery, Classification, and Regression.

Explores Kernel Ridge Regression, the Representer Theorem, and the Kernel Trick in machine learning.

Explains how to find the best hyperplane to separate two classes.

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

Explores the Gaussian Discriminant Rule for classification using Gaussian Mixture Models and discusses drawing boundaries and model complexity.

Covers the derivation of the stochastic gradient descent formula for a simple perceptron and explores the geometric interpretation of classification.

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