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Master advanced ML/DS statistics
·
Module 9
Bayesian Methods for Machine Learning
Frequentist vs. Bayesian Inference
Contrast the frequentist and Bayesian approaches to statistical inference.
2
Bayes' Theorem: Priors and Posteriors
Specify prior and posterior distributions in the context of Bayes' theorem.
3
Conjugate Priors and Posterior Simplification
Explain the role of conjugate priors in simplifying posterior computation.
4
Conjugate Priors and Posterior Distributions
Compute the posterior distribution for a simple model with a conjugate prior.
5
Bayesian Credible Intervals vs. Frequentist Confidence Intervals
Construct and interpret Bayesian credible intervals and compare them to frequentist confidence intervals.
6
Bayesian Linear Regression
Apply Bayesian inference to a linear regression model.
7
Introduction to MAP Estimation
Explain the concept of Maximum A Posteriori (MAP) estimation.
8
Bayesian Model Comparison
Use Bayesian methods for model comparison (e.g., using Bayes factors or posterior odds).
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Unsupervised Learning: Dimensionality Reduction and Clustering
Next module
Neural Network Foundations