Support Vector Machines: Exercises Solutions

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Support Vector Machines: Theory and Applications

Explores Support Vector Machines theory, parameters, uniqueness, and applications in machine learning.

Max-Margin Classifiers

Explores maximizing margins for better classification using support vector machines and the importance of choosing the right parameter.

Linear Models for Classification: Logistic Regression and SVM

Covers linear models for classification, focusing on logistic regression and support vector machines.

Advanced Machine Learning: Brief review of C-SVM

Covers clustering, classification, and Support Vector Machine principles, applications, and optimization, including non-linear classification and Gaussian kernel effects.

Classification Detection

Covers binary hypothesis testing and decision functions in specific scenarios.

Linear Models & k-NN

Covers linear models, logistic regression, decision boundaries, k-NN, and practical applications in authorship attribution and image data analysis.

Feature Expansion and Kernels

Covers feature expansion, kernels, SVM, and nonlinear classification in machine learning.

Linear Models for Classification: Multi-Class Extensions

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

Feature Expansion and Kernel Methods

Explores feature expansion, kernel methods, SVM, and nonlinear classification in machine learning.

Support Vector Machines: SVMs

Explores Support Vector Machines, covering hard-margin, soft-margin, hinge loss, risks comparison, and the quadratic hinge loss.