Basics of Machine Learning
Naive Bayes, decision trees, zero-frequency, missing data, ID3 algorithm, information gain, overfitting, confidence intervals, nearest-neighbour method, Parzen windows, K-D trees, K-means, scree plot, gaussian mixtures, EM algorithm, dimensionality reduction, principal components, eigen-faces, agglomerative clustering, single-link vs. complete link, lance-williams algorithm
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Mixture Models and the EM algorithm (
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Hierarchical Agglomerative Clustering (
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K-means Clustering (
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Principal Component Analysis (
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Max-margin hyperplane, Passive Aggressive algorithm, SVM (
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Decision Tree (
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Nearest Neighbour Method (
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Naive Bayes (
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Generalization and Overfitting (
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