Lecture

Probabilistic Topic Models: Latent Dirichlet Allocation

Related lectures (32)

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

Topic Models: Latent Dirichlet Allocation

Covers topic models, focusing on Latent Dirichlet Allocation, clustering, GMMs, Dirichlet distribution, LDA learning, and applications in digital humanities.

Topic Models: Latent Dirichlet Allocation

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

Topic Models: Understanding Latent Structures

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

Bayesian Networks: Fundamentals and Applications

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

Document Analysis: Topic Modeling

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

Document Analysis and Topic Modeling

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

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 LDA, autoencoders, GANs, and DCGANs.

Common Distributions: Moment Generating Functions

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

Deep Generative Models

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

Word Embeddings: Models and Learning

Explores word embeddings, context importance, and learning algorithms for creating new representations.

Review of Probability

Covers the review of probability concepts including Poisson distribution and moment generating functions.

Latent Semantic Indexing: Concepts and Applications

Explores Latent Semantic Indexing, a technique for mapping documents into a concept space for retrieval and classification.

Embedding Models: Concepts and Retrieval

Covers embedding models for document retrieval, latent semantic indexing, SVD, and topic models.

Probability and Statistics

Covers probability, statistics, independence, covariance, correlation, and random variables.

Statistical Hypothesis Testing

Covers statistical hypothesis testing, confidence intervals, p-values, and significance levels in hypothesis testing.

Asymmetric Parametric Models: Extremal Coefficient

Explores asymmetric parametric models and the extremal coefficient in extreme value dependence.

Text Models: Word Embeddings and Topic Models

Explores word embeddings, topic models, Word2vec, Bayesian Networks, and inference methods like Gibbs sampling.

Fundamental Limits of Gradient-Based Learning

Delves into the fundamental limits of gradient-based learning on neural networks, covering topics such as binomial theorem, exponential series, and moment-generating functions.