Latent and observable variables

Applied sciences
Information engineering
Data science
Graphical models

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (30)

Page 3 of 3

Recommender Systems and Structure Discovery

Explores recommender systems, latent factor models, and clustering algorithms for structure discovery.

Topic Models: Latent Dirichlet Allocation

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

Prediction Tests

Discusses predicting future behavior, identifying outliers, and improving model specification.

Matrix Factorization: Optimization and Evaluation

Explores matrix factorization optimization, evaluation methods, and challenges in recommendation systems.

Mixture models: summary

Summarizes mixtures of logit models, covering various mixing methods and modeling techniques for taste heterogeneity.

Bayesian Networks: Fundamentals and Applications

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

Knowledge Tracing

Delves into Bayesian Knowledge Tracing and Learning Curves, exploring the prediction of student knowledge over time and the importance of accurate performance measurement.

Regression Modeling: Variable Selection

Explores regression modeling, emphasizing the importance of interpretation and prediction in model building.

Model-Based Clustering Methods

Introduces model-based clustering methods using mixture models and latent variables, with practical examples on iris data.

Deep Generative Models: Part 2

Explores deep generative models, including mixtures of multinomials, PCA, deep autoencoders, convolutional autoencoders, and GANs.