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Only one application needs to be completed for all courses desired during the quarter.
Deadline:
03/27/2026
Format:
In-person
Term Offered:
Spring
Overview
This course teaches the mathematical foundations of machine learning (ML) and artificial intelligence (AI). Each week, the course surveys a different algorithm to examine its underlying machinery, covering topics such as linear algebra, calculus, and optimization. ML/AI algorithms range from linear models to gradient boosting, deep learning, and foundation models. Upon course completion, students should be able to learn new ML/AI algorithms independently.
Course Details
At the conclusion of this course, students will be able to:
- Explain the key mathematical ideas that underly different machine learning algorithms.
- Demonstrate proficiency in applying new machine learning algorithms.
- Select the most appropriate machine learning algorithm/analysis strategy to answer their questin of interest.
- Critique and analyze applications of machine learning algorithms.
Machine Learning in R for the Biomedical Sciences: Methods for Prediction, Pattern Recognition, and Data Reduction (DATASCI 216)
This course is part of the Health Data Science Master's and Certificate Program and may have space limitations. Auditing is not permitted.
Exceptions to this prerequisite may be made with the consent of the Course Director, space permitting.
Lectures will be held at 9:30 - 11:00 AM and Labs held 11:00 AM - 12:00 PM, Mondays, March 30 through June 1.
Each week, new material is introduced via an interactive lecture and recommended readings. Learning is reinforced via computer labs, structured discussion sections, and homework.
The quarter's schedule lists dates and times for all activities. All course materials and handouts will be posted on the course's online syllabus.
Software: Python
Grades will be based on total points achieved on the homework assignments and class project. Please note that late assignments will not be 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.
At the conclusion of this course, students will be able to:
- Explain the key mathematical ideas that underly different machine learning algorithms.
- Demonstrate proficiency in applying new machine learning algorithms.
- Select the most appropriate machine learning algorithm/analysis strategy to answer their questin of interest.
- Critique and analyze applications of machine learning algorithms.
Machine Learning in R for the Biomedical Sciences: Methods for Prediction, Pattern Recognition, and Data Reduction (DATASCI 216)
This course is part of the Health Data Science Master's and Certificate Program and may have space limitations. Auditing is not permitted.
Exceptions to this prerequisite may be made with the consent of the Course Director, space permitting.
Lectures will be held at 9:30 - 11:00 AM and Labs held 11:00 AM - 12:00 PM, Mondays, March 30 through June 1.
Each week, new material is introduced via an interactive lecture and recommended readings. Learning is reinforced via computer labs, structured discussion sections, and homework.
The quarter's schedule lists dates and times for all activities. All course materials and handouts will be posted on the course's online syllabus.
Software: Python
Grades will be based on total points achieved on the homework assignments and class project. Please note that late assignments will not be 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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Jean Feng, PhD, MS
Associate Professor, Department of Epidemiology & Biostatistics
Email
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.