ManySpace
Türkçe Sign in Sign up

AI & MLSpecializedAIML-PGM-007

Kalman Filters and Linear-Gaussian State Space Models

Tracking a continuous hidden state: predict, then correct with a gain that weighs model against measurement.

Difficulty
5 / 5
Importance
3 / 5
Study time
1–2 h
Kind of work
Mixed

Why it matters

The learner can vary measurement noise and predict how the Kalman gain shifts trust between prediction and observation.

The route to this topic

Route AIML-PGM-007
Natural Numbers What Is a Model? Variables & Constants Tasks: Regression, Classification, and Beyond Features and Targets Solving Linear Equations Function Notation & Composition Probabilistic Prediction Linear Regression I: Prediction and Residuals Complex Numbers Matrices Intro Loss and Fitting: Squared Error Basic Ratios Unit Circle Matrix Algebra Objective Functions and Empirical Risk Polynomial Functions Likelihood Intuitive Limits Continuity Definition of the Derivative Differentiation Rules Bayesian Reasoning Chain Rule Antiderivatives & Indefinite Integrals Derivatives of Transcendental Functions Riemann Sums & Definite Integral Fundamental Theorem of Calculus Integration by Parts Sample Spaces & Events Conditional Probability & Independence Random Variables & Distributions Joint Distributions Markov Chains Bayesian Networks The Multivariate Gaussian Hidden Markov Models Kalman Filters and Linear-Gaussian State Space Models