CS 464 Introduction to Machine Learning

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: 6, Prerequisite: (CS102 or CS 114) and (MATH 225 or MATH 220 or MATH 241) and (MATH 230 or MATH 255 or MATH 260) .

Autumn Semester (Mehmet Koyut├╝rk)

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