This online course explores the essentials of machine learning and algorithms to help improve learning from data without human intervention.
Topics of the course include: classification and regression, clustering methods, sequential models, matrix factorization, topic modeling and model selection.
Methods include: linear and logistic regression, support vector machines, tree classifiers, boosting, maximum likelihood and MAP inference, EM algorithm, hidden Markov models, Kalman filters, k-means, Gaussian mixture models, among others.
|Teacher:||John W. Paisley|
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