In this course, students will be introduced to supervised learning techniques and algorithms. Coursework covers the following algorithms: linear regression, logistic regression, decision trees, bayesian learning, support vector machines, sequence learning, k-nearest neighbors, and ensemble techniques. The concepts of underfitting, overfitting, cross-validation, and kernel methods will be covered throughout the course. Students will practice building an end-to-end supervised learning project.