Applied Machine Learning

3 credits

Topics in this course include regression (linear regression with multiple features, nearest neighbors & kernel regression, ridge regression), classification (linear classifiers, logistic regression, decision trees, boosting), clustering, and dimensionality reduction. The concepts of overfitting & regularization, feature selection, and performance evaluation are also included. Students will apply these concepts in implementation of practical machine learning applications