Probabilistic Models & Bayesian Thinking

Pro

Medium

ML is fundamentally about uncertainty. Bayes theorem, Gaussian distributions, and maximum likelihood estimation give models a rigorous probabilistic foundation.

Learning Objectives

→Apply Bayes theorem to update beliefs
→Define and maximize log-likelihood
→Understand the Gaussian distribution parameters
→Implement a Naive Bayes classifier

Coding Problems (3)

Gaussian Log-Likelihood

Pro

~20 min· Medium

Naive Bayes Log-Probability

Pro

~20 min· Medium

One Step of a Constant-Velocity Kalman Filter

~20 min· Medium

Concept Check

Optional multiple-choice questions on the ideas behind this section. Most useful after you have tried the coding problems above.