Tutorial: Conditional Random Field (CRF)

Conditional Random Field (CRF) is a probabilistic model for labeling a sequence of words. CRF has found applications in address parsing, NER (names entity recognition), NP chunking etc.

In order to understand how CRF works, it is important to understand the basic concepts like:

• what are probabilistic models
• what are graphical models
• basics of probability
• basic probabilistic models such as naive bayes, maximum entropy and HMM

All these concepts will lead to a concrete understanding of CRFs.  Below presentation will help you in understanding the details of CRF.

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This is the version 1.0 of the presentation I'm developing. I'm planning to add more details like Baum-Welch algorithm, Perceptron/SGD training algorithm, Regularization etc.

Stay tuned and feel free to suggest additional CRF topic to be included.

Basics of Machine Learning (Video)

This is a basic video course on machine learning (ML) that covers the basic theory, algorithms, and applications.

• Lecture 1: The Learning Problem
• Lecture 2: Is Learning Feasible?
• Lecture 3: The Linear Model I
• Lecture 4: Error and Noise
• Lecture 5: Training versus Testing
• Lecture 6: Theory of Generalization
• Lecture 7: The VC Dimension
• Lecture 8: Bias-Variance Tradeoff
• Lecture 9: The Linear Model II
• Lecture 10: Neural Networks
• Lecture 11: Overfitting
• Lecture 12: Regularization
• Lecture 13: Validation
• Lecture 14: Support Vector Machines
• Lecture 15: Kernel Methods
• Lecture 16: Radial Basis Functions
• Lecture 17: Three Learning Principles
• Lecture 18: Epilogue

theory - mathematical
technique - practical
analysis - conceptual