Feature Expansion and Kernels

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Related lectures (31)

Page 3 of 4

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

Neural Networks: Random Features and Kernel Regression

Explores random features in neural networks and kernel regression using stochastic gradient descent.

Kernel Regression: Basics and Applications

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

Kernel Methods: Machine Learning

Explores kernel methods in machine learning, emphasizing their application in regression tasks and the prevention of overfitting.

Linear Models for Classification: Multi-Class Extensions

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

Kernels: Nonlinear Transformations

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

Kernel Regression

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

Linear Models: Recap and Extensions

Covers linear models, multi-class classification, k-Nearest Neighbors, and feature expansion techniques.

Supervised Learning: Classification and Regression

Covers supervised learning, classification, regression, decision boundaries, overfitting, Perceptron, SVM, and logistic regression.

Document Analysis: Topic Modeling

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