Probabilistic latent semantic analysis

Applied sciences
Information engineering
Natural language processing
Topic modelling

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Related lectures (32)

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Topic Models: Understanding Latent Structures

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

Choice models with latent variables: Modeling latent concepts

Explores choice models with latent variables and their estimation process based on likelihood integration.

Topic Models: Latent Dirichlet Allocation

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

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Word Embeddings: Models and Learning

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

Handling Text: Document Retrieval, Classification, Sentiment Analysis

Explores document retrieval, classification, sentiment analysis, TF-IDF matrices, nearest-neighbor methods, matrix factorization, regularization, LDA, contextualized word vectors, and BERT.

Document Analysis: Topic Modeling

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

Recent Advances in Structural Learning for Graphical Models

Covers recent advances in structural learning for graphical models, including Gaussian models, mixed models, and extreme events.

Text Processing: Large Digital Text Collections Analysis

Delves into the processing of large digital text collections, exploring hidden regularities, text reuse, and TF-IDF analysis.

Deep Generative Models

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