CS 464 Introduction to Machine Learning
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Probability and statistics review, estimation (maximum likelihood, maximum a posterior), loss functions, model selection,
feature representation, feature selection, naive Bayes, linear discriminant analysis, logistic regression,
k-nearest neighbor, support vector machines, deep learning, linear regression, decision trees, ensemble methods
(bagging, random forest, boosting) and clustering.
Credit units: 3 ECTS Credit units: 5, Prerequisite:
(CS 102 or CS 114 or CS 115) and (MATH 225 or MATH 220 or MATH 224 or MATH 241) and (MATH 230 or MATH 255 or MATH 260).
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