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Lecture 16: Python Exercise on kNN and PCA, by S. Sarkar

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Lecture 17: Tutorial III, by S. Sarkar

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Lecture 17: Viterbi Decoding for HMM, Parameter Learning, by Pawan Goyal

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Lecture 18: Baum Welch Algorithm, by Pawan Goyal

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Lecture 18: Bayesian Learning, by S. Sarkar

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Lecture 19: Maximum Entropy Models-I, by Pawan Goyal

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Lecture 19: Naive Bayes, by S. Sarkar

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Lecture 2: What Do we Do in NLP, by Pawan Goyal

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Lecture 20 : Bayesian Network, by S. Sarkar

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Lecture 20: Maximum Entropy Models-II, by Pawan Goyal

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Lecture 21: Conditional Random Fields, by Pawan Goyal

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Lecture 21: Python Exercise on Naive Bayes, by S. Sarkar

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Lecture 22: Syntax-Introduction, by Pawan Goyal

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Lecture 22: Tutorial IV, by S. Sarkar

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Lecture 23 : Logistic Regression, by S. Sarkar

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Lecture 23: Syntax-Parsing I, by Pawan Goyal

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Lecture 24: Introduction Support Vector Machine, by S. Sarkar

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Lecture 24: Syntax-CKY, PCFGs, by Pawan Goyal

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Lecture 25: Pcfgs-Inside-Outside Probabilities, by Pawan Goyal

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Lecture 25: SVM : The Dual Formulation, by S. Sarkar

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Lecture 26: Inside-Outside Probabilities, by Pawan Goyal

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Lecture 26: SVM : Maximum Margin with Noise, by S. Sarkar

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Lecture 27: Dependency Grammars and Parsing-Introduction, by Pawan Goyal

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Lecture 27: Nonlinear SVM and Kernel Function, by S. Sarkar

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Lecture 28 : Transition Based Parsing : Formulation, by Pawan Goyal

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Lecture 28: SVM : Solution to the Dual Problem, by S. Sarkar

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Lecture 29 : Transition Based Parsing : Learning, by Pawan Goyal

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Lecture 29: Python Exercise on SVM, by S. Sarkar

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Lecture 3: Why is NLP Hard, by Pawan Goyal

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Lecture 30 : MST-Based Dependency Parsing, by Pawan Goyal

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Lecture 30: Introduction, by S. Sarkar

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Lecture 31: MST-Based Dependency Parsing : Learning, by Pawan Goyal

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Lecture 31: Multilayer Neural Network, by S. Sarkar

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Lecture 32 : Neural Network and Backpropagation Algorithm, by S. Sarkar

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Lecture 32: Distributional Semantics-Introduction, by Pawan Goyal

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Lecture 33: Deep Neural Network, by S. Sarkar

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Lecture 33: Distributional Models of Semantics, by Pawan Goyal

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Lecture 34: Distributional Semantics : Applications, Structured Models, by Pawan Goyal

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Lecture 34: Python Exercise on Neural Network, by S. Sarkar

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Lecture 35: Tutorial VI, by S. Sarkar

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Lecture 35: Word Embeddings-Part I, by Pawan Goyal

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Lecture 36 : Word Embeddings-Part II, by Pawan Goyal

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Lecture 36: Introduction to Computational Learning Theory, by S. Sarkar

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Lecture 37: Lexical Semantics, by Pawan Goyal

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Lecture 37: Sample Complexity : Finite Hypothesis Space, by S. Sarkar

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Lecture 38: Lexical Semantics-Wordnet, by Pawan Goyal

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Lecture 38: VC Dimension, by S. Sarkar

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Lecture 39 : Introduction to Ensembles, by S. Sarkar

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Lecture 39 : Word Sense Disambiguation-I, by Pawan Goyal

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Lecture 4: Empirical Laws, by Pawan Goyal

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