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 (play all)
 Hierarchical Agglomerative Clustering (play all)
 K-means Clustering (play all)
 Principal Component Analysis (play all)
  Max-margin hyperplane, Passive Aggressive algorithm, SVM (play all)
 Decision Tree (play all)
 Nearest Neighbour Method (play all)
 Naive Bayes (play all)
 Generalization and Overfitting (play all)