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  1. From HMMs to CRFs: discriminative learning and features
  2. Structured perceptron, structured SVMs and max-margin Markov networks
  3. Training and optimization
  4. Iterative scaling, L-BFGS, perceptron, MIRA, stochastic and batch gradient descent

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