Kernel (statistics)

Formal sciences
Statistics
Statistical inference
Non-parametric statistics

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Related lectures (32)

Kernel Regression

Covers the concept of kernel regression and making data linearly separable by adding features and using local methods.

Learning the Kernel: Convex Optimization

Explores learning the kernel function in convex optimization, focusing on predicting outputs using a linear classifier and selecting optimal kernel functions through cross-validation.

Kernel Methods: Nonlinear Separation Surfaces

Explores kernel methods for nonlinear separation surfaces using polynomial and Gaussian kernels in Perceptron and SVM algorithms.

Feature Selection, Kernel Regression, Neural Networks Playground

Covers feature selection, kernel regression, and neural networks through exercises.

Nonparametric Statistics: Bayesian Approach

Explores non-parametric statistics, Bayesian methods, and linear regression with a focus on kernel density estimation and posterior distribution.

Information Theory: Source Coding & Channel Coding

Covers the fundamentals of information theory, focusing on source coding and channel coding.

Kernel Methods and Regression

Covers kernel methods, kernel regression, RBF kernel, and SVM for classification.

Kernel Methods: Recap and Applications

Covers polynomial feature expansion, kernel methods, data representations, normalization, and handling imbalanced data in machine learning.

Kernel Methods: Neural Networks

Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.

Kernel Methods: Efficient Dot Product Computation

Demonstrates efficient dot product computation and introduces nonlinear boundaries in the original space.

Data Representation: BoW and Imbalanced Data

Covers overfitting, model selection, validation, cross-validation, regularization, kernel regression, and data representation challenges.

Transformer Architectures: Subquadratic Attention Mechanisms

Covers transformer architecture and subquadratic attention mechanisms, focusing on efficient approximations and their applications in machine learning.

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