Machine Learning in R for the Biomedical Sciences: Methods for Prediction, Pattern Recognition, and Data Reduction
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. 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; and
- Apply the knowledge and techniques to the completion of a real-world biomedical project.
- Opportunities and Challenges of Complex Biomedical Data: Introduction to the Science of "Big Data" (BIOSTAT 202)
- 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)
- Clinical Epidemiology (EPI 204)
Adam Olshen, PhD, MA
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 PM to 10:15 AM, Jan 6 through March 17.
Lecture recordings will be available online later in the day.
Computer Laboratory: Content: Assistance with use of R software and project-specific mentoring.
Time: Wednesdays, 10:30 to 11:30 AM
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.
This course is sponsored by the Training in Clinical Research (TICR) Program, and space is limited. Preference is given to UCSF-affiliated personnel. We regret that auditing in the classroom is not permitted, but most of the course materials (with the exception of videotapes, answer keys, examinations, and copyrighted documents) are freely available (without formal enrollment) on the course’s online syllabus. Many students can glean the majority of the course’s content from this free access, but, importantly, formal enrollment also provides access to faculty for questions and individual-level extension of the curriculum, a community of other engaged students for in-person real-time discussion, and personalized correction and feedback on homework and projects.
To enroll in this course, please fill out and submit the application below. Please see our fees page for cost information. The deadline for application is December 14, 2020. Only one application needs to be completed for all courses desired during the quarter.
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Information for how to pay;
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