Support Vector Machines: Basics and Applications

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Related lectures (30)

Page 2 of 3

Covers the history and applications of SVM, as well as the construction of linear classifiers and the concept of classifier margin.

Kernel Methods: SVM and Regression

Introduces kernel methods like SVM and regression, covering concepts such as margin, support vector machine, curse of dimensionality, and Gaussian process regression.

Unsupervised Learning: Dimensionality Reduction

Explores unsupervised learning techniques for reducing dimensions in data, emphasizing PCA, LDA, and Kernel PCA.

Feature Maps and Kernels

Covers feature maps, Representer theorem, kernels, and RKHS in machine learning.

Kernel Ridge Regression: Equivalence, Representer Theorem, and Kernel Trick

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

Linear Models for Classification: Part 3

Explores linear models for classification, including binary classification, logistic regression, decision boundaries, and support vector machines.

Statistical Inference and Machine Learning

Covers statistical inference, machine learning, SVMs for spam classification, email preprocessing, and feature extraction.

Mercer Theorem and Kernels

Explores the Mercer Theorem, Kernels, and their role in machine learning applications.

Kernel Ridge Regression: Equivalent Formulations and Representer Theorem

Explores Kernel Ridge Regression, equivalent formulations, Representer Theorem, Kernel trick, and predicting with kernels.

Machine Learning Fundamentals

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