Lecture

Topic Models: Latent Dirichlet Allocation

Related lectures (28)

Topic Models: Understanding Latent Structures

Explores topic models, Gaussian mixture models, Latent Dirichlet Allocation, and variational inference in understanding latent structures within data.

Topic Models

Introduces topic models, covering clustering, GMM, LDA, Dirichlet distribution, and variational inference.

Topic Models: Latent Dirichlet Allocation

Introduces Latent Dirichlet Allocation for topic modeling in documents, discussing its process, applications, and limitations.

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.

Document Analysis: Topic Modeling

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

Clustering & Density Estimation

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

Dimensionality Reduction: PCA and LDA

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

Gaussian Mixture Models: Data Classification

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

Document Analysis and Topic Modeling

Covers document analysis, topic modeling, and deep generative models, including autoencoders and GANs.

Deep Generative Models

Covers deep generative models, including LDA, autoencoders, GANs, and DCGANs.

Probabilistic Topic Models: Latent Dirichlet Allocation

Explores Latent Dirichlet Allocation, a probabilistic topic model for document clustering and analysis using distributions over words and topics.

Variational Inference and Neural Networks

Covers variational inference and neural networks for classification tasks.

Dirichlet-Multinomial Model

Discusses the Dirichlet distribution, Bayesian inference, posterior mean and variance, conjugate priors, and predictive distribution in the Dirichlet-Multinomial model.

Deep Generative Models

Covers deep generative models, including variational autoencoders, GANs, and deep convolutional GANs.

Bayesian Networks: Fundamentals and Applications

Covers the fundamentals of Bayesian Networks and their applications in probabilistic topic modeling.

Supervised Learning Fundamentals

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Spin Glasses and Bayesian Estimation

Covers the concepts of spin glasses and Bayesian estimation, focusing on observing and inferring information from a system closely.

Boltzmann Machine

Introduces the Boltzmann Machine, covering expectation consistency, data clustering, and probability distribution functions.

Common Distributions: Moment Generating Functions

Explores common probability distributions, special distributions, and entropy concepts.