Subject: Data Science
Course Number: 216
Course Title: Machine Learning in R for the Biomedical Sciences
Units: 3
School: Graduate Division
Department: Health Data Science Program

Course Description: This is a course that covers machine learning methods as they apply to areas of biomedical research and will teach how to implement the methods in R. Topics to be covered include: What is Machine learning? Prediction techniques (including classification) and methods for assessing them, Cross-validation, penalized regression methods such as lasso, boosting, bagging and ensemble methods, pattern recognition, deep learning, and data reduction methods, and machine learning meta packages in R.
Prerequisites: BIOSTAT 208, DATASCI 213 & BIOSTAT 209. Exceptions to these prerequisites may be made with the consent of the Course Director, space permitting. Strongly recommended: EPIDEMIOL 204 & DATASCI 202
Restrictions: This is a core course of the Health Data Science (HDS) program and part of the Training in Clinical Research Program and may have space limitations. Auditing is not permitted.
Activities: Direct - Lecture, Direct - Lab-Skills, Direct - Project, Student - Lecture, Student - Project

Instructor of Record: A. Olshen
May the student choose the instructor for this course? No
Does enrollment in this course require instructor approval? No

Quarter(s) Offered: Winter
Course Grading Convention: Letter Grade, P/NP (Pass/Not Pass) or S/U (Satisfactory/Unsatisfactory)
Graduate Division course: Yes
Is this an Interprofessional Education (IPE) course? No
May students in the Graduate Division (i.e. pursuing Master or PhD) enroll in this course? Yes
Repeat course for credit? No