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This course covers statistical methods in machine learning such as decision trees, random forests and support vector machines The course will use a project-based approach to give students hands-on experience using these techniques by analyzing large and complex real-world datasets. More importantly, they will learn the statistical principles behind these procedures, such as loss functions, maximum likelihood estimation and bias-variance trade-off as well as why these principles matter in real world settings.