Lecture
Dimensionality Reduction: PCA and LDA
Related lectures (170)
Clustering & Density Estimation
Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.
Clustering & Density Estimation
Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.
Clustering & Density Estimation
Covers dimensionality reduction, clustering, and density estimation techniques, including PCA, K-means, GMM, and Mean Shift.
Document Analysis: Topic Modeling
Explores document analysis, topic modeling, and generative models for data generation in machine learning.
Clustering: k-means
Explains k-means clustering, assigning data points to clusters based on proximity and minimizing squared distances within clusters.
Gaussian Mixture Models: Data Classification
Explores denoising signals with Gaussian mixture models and EM algorithm, EMG signal analysis, and image segmentation using Markovian models.
Data Representation: PCA
Covers data representation using PCA for dimensionality reduction, focusing on signal preservation and noise removal.
Unsupervised Learning: PCA & K-means
Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.
Machine Learning Fundamentals
Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
Clustering: Theory and Practice
Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.