Machine Learning

3 credits · Graduate School

This is the first course in a sequence exploring statistical modeling and machine learning techniques. Both courses emphasize unifying and high-level concepts, such as prediction and calibration, classification and discrimination, optimism and cross-validation, re-sampling methods for model assessment, the evaluation of modeling assumptions and bias-variance trade-off. This first course focuses on regression, generalized linear models, regularized regression, kernel methods, bagging and boosted trees, and simple neural networks. [3]