Data Classification: Mixture Models

Related lectures (29)

Page 2 of 3

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

Discusses statistical view of neural networks, classification tasks, and cross-entropy loss functions.

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

Covers unsupervised learning, dimensionality reduction, SVD, low-rank estimation, PCA, and Monte Carlo Markov Chains.

Covers the training process of a neural network, including feedforward, cost function, gradient checking, and visualization of hidden layers.

Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.

Explores parametric spectrum estimation methods, including line and smooth spectra, and delves into heart rate variability analysis.

Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.

Covers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.

Explores latent variable models, EM algorithm, and Jensen's inequality in statistical modeling.