Bayesian Networks: Fundamentals and Applications

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Related lectures (30)

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Mixture Models: Simulation-based Estimation

Explores mixture models, including discrete and continuous mixtures, and their application in capturing taste heterogeneity in populations.

Probability Theory: Midterm Solutions

Covers the solutions to the midterm exam of a Probability Theory course, including calculations of probabilities and expectations.

Statistical Modelling & Probability Basics

Covers statistical modelling basics, probability concepts, and random variables for data analysis.

Joint Distributions

Explores joint distributions, marginal laws, covariance, correlation, and variance properties.

Probability Distributions

Covers various probability distributions and their applications in real-world scenarios.

Families of Distributions: PDFs and CDFs

Explores PDFs and CDFs for discrete and continuous random variables, including Gaussian, uniform, and exponential distributions.

Latent Semantic Indexing: Concepts and Applications

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

Copulas and Margins

Explores copulas with different margins and their joint distributions, including concepts like concordance probability and extremal dependence.

Unsupervised Learning: PCA & K-means

Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.

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.