Hyperplane separation theorem

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Covers the basics of linear regression, focusing on minimizing errors and predicting outputs.

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

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

Covers examples of implicit differentiation and solving equations involving trigonometric functions.

Explores the philosophy of machine learning and the importance of smoothness assumptions in training sets.

Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.

Covers the concept of a simple perceptron and its ability to solve linearly separable problems by imposing a separating hyperplane.

Explains differentiability in multivariable functions and the geometric interpretation of tangent planes.

Covers Farkas' lemma, exploring the relationship between linear programs and the conditions for its validity.

Explores convex optimization duality, KKT conditions, and financial arbitrage detection.

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