Course Catalog » Biostatistics 216

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 202, BIOSTAT 208, prior completion or concurrent enrollment in BIOSTAT 209, and BIOSTAT 213. EPI 204 is highly recommended.
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 - Project, Student - Lecture

Instructor of Record: J. Feng
Additional Instructor(s): M. Segal, J. Kornak
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