Feature Maps and Kernels

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Related lectures (32)

Page 1 of 4

Explores kernels for simplifying data representation and making it linearly separable in feature spaces, including popular functions and practical exercises.

Kernel Regression: Basics and Applications

Explores kernel regression, the curse of dimensionality, and random features in neural networks.

Mercer Theorem and Kernels

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

Introduction to Machine Learning: Linear Models

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.

Feature Expansion: Kernels and KNN

Covers feature expansion, kernels, and K-nearest neighbors, including non-linearity, SVM, and Gaussian kernels.

Landscape and Generalisation in Deep Learning

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

Kernel Regression: K-nearest Neighbors

Covers the concept of kernel regression and K-nearest neighbors for making data linearly separable.

Neural Networks Recap: Activation Functions

Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.

Feature Expansion and Kernels

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

Support Vector Machines: Kernel Tricks

Explores kernel tricks in support vector machines for efficient computation in high-dimensional spaces without explicit transformation.