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,
knearest 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 Koyutürk)



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