Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Home

Lecture

Feature Expansion and Kernel Methods

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (55)

Linear Models & k-NN Log in to Mediaspace to watch this video

Covers linear models, logistic regression, decision boundaries, k-NN, and practical applications in authorship attribution and image data analysis.

Untitled Log in to Mediaspace to watch this video

Document Analysis: Topic Modeling Log in to Mediaspace to watch this video

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

Nonparametric Regression: Kernel-Based Estimation Log in to Mediaspace to watch this video

Covers nonparametric regression using kernel-based estimation techniques to model complex relationships between variables.

Linear Models: Part 1 Log in to Mediaspace to watch this video

Covers linear models, including regression, derivatives, gradients, hyperplanes, and classification transition, with a focus on minimizing risk and evaluation metrics.

Nonlinear Machine Learning: k-Nearest Neighbors and Feature Expansion Log in to Mediaspace to watch this video

Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.

Support Vector Machines: Basics and Applications Log in to Mediaspace to watch this video

Covers the basics of support vector machines, logistic regression, decision boundaries, and the k-Nearest Neighbors algorithm.

Linear Models: Classification Basics Log in to Mediaspace to watch this video

Explores linear models for classification, logistic regression, SVM, k-NN, and curse of dimensionality.

Kernel Methods in Machine Learning: Kernel Regression and SVM Log in to Mediaspace to watch this video

Discusses kernel methods in machine learning, focusing on kernel regression and support vector machines, including their formulations and applications.

Feature Selection, Kernel Regression, Neural Networks Playground Log in to Mediaspace to watch this video

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

Kernel Regression: Basics and Applications Log in to Mediaspace to watch this video

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

Linear Models for Classification: Multi-Class Extensions Log in to Mediaspace to watch this video

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

Clustering & Density Estimation Log in to Mediaspace to watch this video

Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.

Clustering & Density Estimation Log in to Mediaspace to watch this video

Covers dimensionality reduction, clustering, and density estimation techniques, including PCA, K-means, GMM, and Mean Shift.

Deep Learning: Multilayer Perceptron and Training Log in to Mediaspace to watch this video

Covers deep learning fundamentals, focusing on multilayer perceptrons and their training processes.

Clustering & Density Estimation Log in to Mediaspace to watch this video

Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.

Linear and Weighted Regression: Solution to Exercises Log in to Mediaspace to watch this video

Covers exercises on linear and weighted regression solutions, SVR problems, and Regular Least Squares.

General Linear Model: Model Selection Log in to Mediaspace to watch this video

Explores the General Linear Model, significance testing, model selection, and parameter inference.

Optimization in Statistics and Machine Learning: Maximum Likelihood Estimation Log in to Mediaspace to watch this video

Explores Maximum Likelihood Estimation, linear models, logistic regression, and Support Vector Machines.

Kernel Ridge Regression: Equivalent Formulations and Representer Theorem Log in to Mediaspace to watch this video

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

Page 2 of 3