Bayesian Inference for Machine Learning

• Define the role of initial probability in statistical reasoning

• Identify how new information updates existing statistical models

• Calculate the probability of observed data given a hypothesis

• Apply the fundamental equation of Bayesian inference

• Interpret the result of a Bayesian update

• Simplify complex probability calculations using normalization

• Select appropriate priors for different machine learning tasks

• Use conjugate priors to streamline Bayesian calculations

• Estimate the total probability of observed data

• Estimate parameters using Bayesian methodologies

• Build a basic Bayesian text classifier

• Implement linear regression with Bayesian uncertainty

• Apply Monte Carlo methods to approximate complex distributions

• Evaluate model performance using Bayesian criteria

• Deploy Bayesian inference within a larger machine learning pipeline