Undergraduate Certificate in Bayesian Data Analysis in AI Systems
-- ViewingNowThe Undergraduate Certificate in Bayesian Data Analysis in AI Systems is a comprehensive course designed to meet the growing industry demand for professionals skilled in Bayesian data analysis and artificial intelligence. This certificate course emphasizes the importance of probability theory and statistical inference, equipping learners with essential skills to tackle real-world AI problems using Bayesian methods.
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โข Introduction to Bayesian Data Analysis: Basic principles, concepts, and benefits of Bayesian data analysis. Understanding probability, likelihood, and priors.
โข Probabilistic Graphical Models: Directed and undirected graphs, Bayesian networks, and Markov random fields. Inference and learning in probabilistic graphical models.
โข Conjugate Priors and Posteriors: Conjugate distributions, analytical solutions, and their applications in Bayesian analysis.
โข MCMC Methods for Bayesian Inference: Overview, advantages, and limitations of Markov Chain Monte Carlo methods. Metropolis-Hastings, Gibbs sampling, and the No-U-Turn sampler.
โข Hierarchical Bayesian Models: Modeling complex systems with multiple levels of uncertainty. Sharing statistical strength across related parameters.
โข Bayesian Model Selection and Comparison: Bayes factors, marginal likelihood, deviance information criteria, and other methods for model evaluation.
โข Bayesian Machine Learning: Bayesian treatment of popular machine learning algorithms, including linear regression, logistic regression, and neural networks.
โข Bayesian Deep Learning: Bayesian methods for deep learning, including variational inference, dropout approximations, and Monte Carlo methods.
โข Practical Bayesian Data Analysis: Hands-on experience with popular probabilistic programming languages, such as Stan and PyMC3, to analyze real-world datasets.
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