Subject: Biostatistics
Course Number: 216
Course Title: Machine Learning in R for the Biomedical Sciences
Units: 3
School: Graduate Division
Department: Clinical Research 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, BIOSTAT 213 & BIOSTAT 209. Exceptions to these prerequisites may be made with the consent of the Course Director, space permitting. Strongly recommended: EPI 204 & BIOSTAT 202
Restrictions: This course is part of the Training in Clinical Research (TICR) 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 a web-based online course? No
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