Model-Based Clustering Methods

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Related lectures (30)

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

Clustering: Unsupervised Learning

Explores dimensionality reduction, clustering algorithms, and the state of machine learning.

Unsupervised Behavior Clustering

Explores unsupervised behavior clustering and dimensionality reduction techniques, covering algorithms like K-Means, DBSCAN, and Gaussian Mixture Model.

ANOVA & Moderation

Covers ANOVA, mediation, and moderation analyses in R for CS-411 Digital Education.

Unsupervised Learning: Clustering Methods

Covers unsupervised learning focusing on clustering methods and the challenges faced in clustering algorithms like K-means and DBSCAN.

Clustering: Theory and Practice

Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.

Binomial Distribution in R

Introduces the Binomial distribution in R with examples of n coin tosses.

Exploratory Data Analysis: Binning and Histograms

Covers statistical frequency, binning, histograms, and R data analysis basics.

Gaussian Mixture Models: Data Classification

Explores denoising signals with Gaussian mixture models and EM algorithm, EMG signal analysis, and image segmentation using Markovian models.

Model Formulas in R: Basics and Applications

Explores model formulas in R, covering basics, interactions, ANOVA, and factorial designs with practical examples.