Machine Learning in R for the Biomedical Sciences: Methods for Prediction, Pattern Recognition, and Data Reduction

Winter 2023 (3 units)

This course covers machine learning methods for solving problems in biomedical research. Machine learning algorithms extract patterns from data to perform tasks such as prediction, clustering, and dimension reduction. Machine learning lies at the intersection between statistics and computer science. The techniques differ from traditional methods in that they scale with the size and complexity of the data. Course topics include supervised learning, unsupervised learning, evaluation/validation of machine learning algorithms, penalization methods for high-dimensional data, ensemble methods, and deep learning. Students will learn to apply these methods in R.
 

Objectives

The course objectives are:

  • Understand the rationale and mechanics of common machine learning techniques.
  • Learn how to evaluate and validate machine learning algorithms.
  • Be able to apply machine learning techniques in R.
  • Apply the knowledge and techniques to the completion of a real-world biomedical project.

Prerequisites

Prior completion or equivalent experience:

Biostatistical Methods for Clinical Research II (BIOSTAT 208)
Introduction to Computing in the R Software Environment (BIOSTAT 213)

Prior completion or concurrent enrollment:
Biostatistical Methods for Clinical Research III (BIOSTAT 209)

Highly recommended:
Clinical Epidemiology (EPI 204)
Opportunities and Challenges of Complex Biomedical Data: Introduction to the Science of "Big Data" (BIOSTAT 202)

Faculty

Course Director:

Jean Feng, PhD, MS

Assistant Professor, Epidemiology & Biostatistics
email: [email protected]

Format

Each week, new material is introduced via an interactive lecture and recommended readings. Learning is reinforced via computer labs, structured discussion sections, and homework.

Lectures: Wednesdays, 8:45 AM - 10:15 AM. Jan 11 through Mar 22.  Lectures will be on zoom. Lecture recordings will be available online later in the day.
Computer Laboratory:  Fridays, 3:15 PM - 4:15 PM.  Jan 13 through Mar 23

The schedule for the quarter shows dates and times for all activities.

Materials

Software
R
Rstudio

All course materials and handouts will be posted on the course's online syllabus.

Grading

Grades will be based on total points achieved on the homework assignments and class project. Please note that late assignments are not accepted.

To Enroll

ATCR and MAS students use the Student Portal

Students taking individual courses:

Course Fees
How to pay (please read before applying)
Only one application needs to be completed for all courses desired during the quarter.

Winter Course Schedule (available Oct 1)

Apply (application for Winter will be available November 15, 2022)