Course Title: Applied Machine Learning
Course ID: BST263 (Full Term Course)
Topics to be covered:
The course will consist of a series of modules, building up from foundations, through linear, non-linear, and nonparametric supervised methods, as well as covering unsupervised learning and Bayesian methods. Along the way, the students will learn how to evaluate model performance/accuracy, quantify uncertainty, and combine methods via ensembles. The students will gain hands-on experience implementing and applying the methods in lab exercises and homework programming assignments, while learning the conceptual foundations in homework problem sets.
Prerequisites: BST 260 or BST 210 or BST 232, or an equivalent course
Prior math/stat knowledge: Ask the course instructor
Computing: R will be used in lecture and lab sessions