This module explores essential concepts and techniques in machine learning, including key topics such as linear methods (e.g., linear regression, lasso, ridge), tree methods (decision trees, random forests, boosting), and unsupervised methods like clustering. Learners will gain proficiency fitting various machine learning models, understanding regularization to prevent overfitting, and cross-validation to evaluate model performance and tune hyper-parameters.

Note that this information may change at any time. Please visit the MyBU Student Portal for the most up-to-date course information.