Document Analysis and Topic Modeling

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Related lectures (29)

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Dimensionality Reduction: PCA and Kernel PCA

Covers PCA, Kernel PCA, and autoencoders for dimensionality reduction in data analysis.

Variational Autoencoders

Covers Variational Autoencoders, a probabilistic approach to autoencoders for data generation and feature representation, with applications in Natural Language Processing.

Bayesian Networks: Fundamentals and Applications

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

Unsupervised Learning: Clustering & Dimensionality Reduction

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Multivariate Statistics: Normal Distribution

Introduces multivariate statistics, covering normal distribution properties and characteristic functions.

Nearest Neighbor Rules: Part 2

Explores the Nearest Neighbor Rules, k-NN algorithm challenges, Bayes classifier, and k-means algorithm for clustering.

Diffusion Models

Explores diffusion models, focusing on generating samples from a distribution and the importance of denoising in the process.

Encoder for Force and Vision in Manipulation

Explores combining force and vision for efficient manipulation tasks using a lower dimensional representation.

Generative Models: Trajectory Forecasting

Explores generative models for trajectory forecasting in autonomous vehicles, including discriminative vs generative models, VAES, GANS, and case studies.