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Lecture 1Introduction, Knowledge Discovery Process, by Pabitra Mitra

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Lecture 25 : Support Vector Machine IV, by Pabitra Mitra

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Lecture 24 : Support Vector Machine III, by Pabitra Mitra

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Lecture 3Data Preprocessing-II, by Pabitra Mitra

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Lecture 35: Clustering IV, by Pabitra Mitra

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Lecture 20, by Pabitra Mitra

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Lecture 23 : Support Vector Machine II, by Pabitra Mitra

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Lecture 7 : Classification, by Pabitra Mitra

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Lecture 21, by Pabitra Mitra

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Lecture 33: Clustering II, by Pabitra Mitra

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Lecture 41 : Dimensionality Reduction-I, by Pabitra Mitra

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Lecture 17 : K Nearest Neighbor I, by Pabitra Mitra

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Lecture 18 : K Nearest Neighbor II, by Pabitra Mitra

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Lecture 6 : Rule Generation, by Pabitra Mitra

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Lecture 30: Artificial Neural Networks III, by Pabitra Mitra

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Lecture 2Data Preprocessing-I, by Pabitra Mitra

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Lecture 4Association Rules, by Pabitra Mitra

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Lecture 31: Artificial Neural Networks IV, by Pabitra Mitra

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Lecture 29:Artificial Neural Networks II, by Pabitra Mitra

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Lecture 42 : Dimensionality Reduction-II, by Pabitra Mitra

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Lecture 39 : Regression III, by Pabitra Mitra

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Lecture 26 : Support Vector Machine V, by Pabitra Mitra

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Lecture 16 : Bayes Classifier V, by Pabitra Mitra

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Lecture 12 : Bayes Classifier I, by Pabitra Mitra

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Lecture 27: Kernel Machines, by Pabitra Mitra

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Lecture 5Apriori Algorithm, by Pabitra Mitra

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Lecture 37 : Regression I, by Pabitra Mitra

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Lecture 19 :, by Pabitra Mitra

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Lecture 38 : Regression II, by Pabitra Mitra

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Lecture 15 : Bayes Classifier IV, by Pabitra Mitra

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