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).
Autumn Semester (Ayşegül Dündar Boral)
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