Textual Data Analysis: Classification & Dimensionality Reduction

Related lectures (31)

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

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

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

Covers the concept of multiclass classification and the challenges of linearly separating data with multiple classes.

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

Explores the Nearest Neighbor Rules, k-NN algorithm challenges, Bayes classifier, and k-means algorithm for clustering.

Covers logistic regression's probabilistic interpretation, multinomial regression, KNN, hyperparameters, and curse of dimensionality.

Explores the applications and limitations of Principal Component Analysis, including denoising, compression, and regression.

Explores PCA, Autoencoders, and their applications in dimensionality reduction and data generation.

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Covers Principal Component Analysis for dimension reduction in biological data, focusing on visualization and pattern identification.

Page 2 of 2