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Only one application needs to be completed for all courses desired during the quarter.
Deadline:
01/19/2027
Format:
In-person
Term Offered:
Winter
Overview
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.
Course Details
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.
Prior completion or equivalent experience:
- Biostatistical Methods for Clinical Research II (BIOSTAT 208)
Programming for Health Data Science in R (DATASCI/BIOSTAT 213)
Prior completion or concurrent enrollment:
Highly recommended:
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: Lectures will be in-person. Lecture recordings will be available online later in the day.
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.
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.
For UC-Affiliated learners, the course fee is $3,200
For Non-UC-Affiliated learners, the course fee is $3,800
UC-Affiliation: Concurrent enrollment in a University of California-sponsored residency or post-doctoral fellowship program that is recognized by the Office of Graduate Medical Education; or a registered student in one of the professional schools or graduate programs at the University of California (in a program other than the TICR program); or individuals who hold full-time salaried University of California faculty, academic or staff positions. Please note: Individuals will be asked to provide proof of UC status.
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.
Prior completion or equivalent experience:
- Biostatistical Methods for Clinical Research II (BIOSTAT 208)
Programming for Health Data Science in R (DATASCI/BIOSTAT 213)
Prior completion or concurrent enrollment:
Highly recommended:
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: Lectures will be in-person. Lecture recordings will be available online later in the day.
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.
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.
For UC-Affiliated learners, the course fee is $3,200
For Non-UC-Affiliated learners, the course fee is $3,800
UC-Affiliation: Concurrent enrollment in a University of California-sponsored residency or post-doctoral fellowship program that is recognized by the Office of Graduate Medical Education; or a registered student in one of the professional schools or graduate programs at the University of California (in a program other than the TICR program); or individuals who hold full-time salaried University of California faculty, academic or staff positions. Please note: Individuals will be asked to provide proof of UC status.
Meet the Faculty
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How to Enroll
Whether you're exploring the field, enhancing your skills, or working toward a larger academic goal, most individual courses are open to everyone. Current learners enrolled in DEB Education programs have priority in enrollment for this course. Any extra spaces are available to non-DEB Education learners (i.e., those from the Institute for Global Health Sciences, School of Nursing, external groups, etc).
You’ll find details on course fees, how to apply and pay, and the full course schedule on the Course Enrollment & Payment page. Be sure you understand the enrollment policies and payment process before completing the enrollment form.
And remember: you only need to submit one form for all the new courses you want to take.