Feature Maps and Kernels

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Related lectures (32)

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Vector Spaces: Linear Applications and Generators

Introduces vector spaces, linear applications, generators, and dimensionality in mathematics.

Regression: Exercises

Covers exercises on regression functions using RLS, WLS, and LWR.

Document Analysis: Topic Modeling

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

Kernel Trick: Understanding Machine Learning

Explores the kernel trick in machine learning, enabling high-dimensional operations without explicit coordinate calculations.

Dimensionality Reduction: PCA & t-SNE

Explores PCA and t-SNE for reducing dimensions and visualizing high-dimensional data effectively.

Nonlinear SVM: Kernels and Dual Optimization

Explores transforming data with nonlinear maps, kernels, dual optimization, and interpreting SVM results.

Dimensionality Reduction: PCA and LDA

Covers dimensionality reduction techniques like PCA and LDA, clustering methods, density estimation, and data representation.

Support Vector Machines: Basics and Applications

Covers the basics of Support Vector Machines, including linear separability, hyperplanes, margins, and non-linear SVM with kernels.

Dimensionality Reduction: PCA & LDA

Covers PCA and LDA for dimensionality reduction, explaining variance maximization, eigenvector problems, and the benefits of Kernel PCA for nonlinear data.

Machine Learning Fundamentals

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.