Intro to Machine Learning

3 Credits · Level: Undergraduate

This undergraduate course surveys the major research areas of machine learning focusing on classification. Through traditional lectures and programming projects, students learn (1) to understand the foundations of machine learning, (2) to design and implement methods of machine learning, (3) to evaluate methods of machine learning, and (4) to conduct empirical evaluations of multiple methods of machine learning. The course compares and contrasts machine learning with related endeavors, such as statistical learning, pattern classification, data mining, and information retrieval. Topics include instance-based approaches, naive Bayes, decision trees, rule induction, linear classifiers, support vector machines, neural networks, ensemble methods, evaluation, and applications. Students complete five programming projects using Java. There are midterm and final exams.