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AI & MLSpecializedAIML-PGM-011

Conditional Random Fields

Discriminative undirected models for structured outputs: logistic regression over whole label sequences.

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

Why it matters

The learner can explain how a linear-chain CRF scores a label sequence with transition and emission features and why it avoids HMM independence assumptions.

The route to this topic

Route AIML-PGM-011
Natural Numbers What Is a Model? Variables & Constants Tasks: Regression, Classification, and Beyond Features and Targets Solving Linear Equations Function Notation & Composition Classification Framing Linear Regression I: Prediction and Residuals Complex Numbers Scores and Logits Loss and Fitting: Squared Error Basic Ratios Unit Circle Sigmoid and Probabilities Objective Functions and Empirical Risk Polynomial Functions Logistic Regression 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 Bayesian Networks Markov Random Fields Conditional Random Fields