bayesian
3 free lessons tagged bayesian across Robotics, Math. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
The Kalman filter: optimal state estimation from noisy measurements
How the Kalman filter fuses a motion model with noisy measurements by carrying a Gaussian belief, growing uncertainty on predict and shrinking it on update, weighting the two by the Kalman gain, plus the EKF and UKF for nonlinear systems.
Random Variables and Distributions
Build the vocabulary that underlies all of ML: sample spaces, discrete and continuous random variables, PMFs, PDFs, and CDFs. Then tour the key distributions — Bernoulli, Binomial, Categorical, Gaussian, Poisson, Exponential, Uniform — with their parameters, mean, variance, and exactly when each appears in practice.
Bayesian Inference
Understand what it really means to update beliefs with data. Derive Bayes' theorem from first principles, dissect the roles of prior, likelihood, posterior, and evidence, work through a complete Beta-Binomial conjugate example numerically, and see why the base-rate fallacy trips up even experts.