Machine Learning in R for the Biomedical Sciences: Methods for Prediction, Pattern Recognition, and Data Reduction (BIOSTAT 216)
Winter 2025 (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: |
Assistant Professor, Epidemiology & Biostatistics |
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. 8 through Mar 19. Lectures will be in-person. Lecture recordings will be available online later in the day.
Computer Laboratory: Thursdays, 3:15 PM - 4:15 PM. Jan 9 through Mar 20
The schedule for the quarter shows dates and times for all activities. All course materials and handouts will be posted on the course's online syllabus.
Materials
Grading
Grades will be based on total points achieved on the homework assignments and class project. Please note that late assignments are not accepted.
Only UCSF students (defined as individuals enrolled in UCSF degree or certificate programs) will receive academic credit for courses. Official transcripts are available to UCSF students only. A Certificate of Course Completion will be available upon request to individuals who are not UCSF students and satisfactorily pass all course requirements.
To Enroll
ATCR and MAS students use the Student Portal
Students taking individual courses:
Course Fees
How to pay (please read before applying)
Winter 2025 Course Schedule
Apply by January 6, 2025