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Final Examination and Paper.pdf

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Lecture 1 - Introduction and Expectations.pdf

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Lecture 2 - Overview of Data Mining.pdf

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Lecture 3 - Data Set Quality and Sources of Data, Descriptive Statistics.pdf

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Lecture 4 - Naïve Bayes and Nearest Neighbor Classification.pdf

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Lecture 5 - Decision Tree Induction.pdf

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Lecture 6 - Entropy based Tree Induction.pdf

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Lecture 7 - Frequency Tables GINI Index and 𝜒^2 for Attribute Selection.pdf

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Lecture 8 - Estimating Classifier Accuracy.pdf

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Lecture 9 - Continuous Attributes.pdf

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Lecture 10- Overfitting.pdf

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Lecture 11 - More on Entropy.pdf

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Lecture 12 - Association Rule Mining - Apriori.pdf

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Lecture 13 - Rule Interestingness.pdf

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Lecture 14 - Basic Clustering.pdf

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Lecture 15 - Sequence Mining.pdf

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Lecture 16 - Rule Induction - PRISM Algorithm.pdf

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Lecture 17 - More On Clustering.pdf

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