Advanced Machine Learning for the Biomedical Sciences II
This course builds upon the introduction to machine learning taught in Machine Learning in R for the Biomedical Sciences: Methods for Prediction, Pattern Recognition, and Data Reduction (BIOSTAT 216) to provide a deeper mathematical and statistical understanding of machine learning algorithms. The applied focus is on solving problems of prediction, pattern recognition and data reduction in the biomedical sciences, but other applications will also be mentioned. Instruction includes how to manipulate and customize popular machine learning algorithms to best satisfy specific research needs. The R software environment will be used throughout.
The objectives are to:
- Understand mathematical and statistical foundations of several contemporary machine learning algorithms;
- Select among available machine learning algorithms to identify the most appropriate for a given research question; and
- Apply and customize state-of-the-art machine learning algorithms to tabular data, biomedical imaging data, sequence data, and time series to address research questions in the biomedical sciences.
- Introduction to Computing in the R Software Environment (BIOSTAT 213), or equivalent experience;
- Machine Learning in R for the Biomedical Sciences: Methods for Prediction, Pattern Recognition, and Data Reduction (BIOSTAT 216), or equivalent experience; and
- Biostatistical Methods for Clinical Research II (BIOSTAT 208), or equivalent experience.
Each week, new material is introduced via recommended reading and interactive lectures, lasting between 60 to 90 minutes, in which discussion is encouraged. Homework assignments, designed to reinforce the core concepts, will be given every other week. The philosophy of the course is to steadily build a knowledge base over the course of the academic quarter, and that ample time is needed between each new installment of material to optimize comprehension.
Content: Introduction of new material. Interaction and discussion are encouraged. Lecture recordings will be available online later in the day.
Time: Thursdays, 1:15 to 2:45 PM.
Large Group Discussion
Time: Thursdays, 2:45 to 4:15 PM.
The syllabus for the quarter shows dates and times for all activities.
Pattern Recognition and Machine Learning by B. Christopher. Springer Publishers. 2007.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction by H. Trevor, T. Robert and F. Jerome. Springer Publishers. 2nd Edition. 2009.
Books may be purchased either through the publisher or a variety of commercial venues (e.g., Amazon.com).
All course materials and handouts will be posted on the course's syllabus.
Grades will be based on total points achieved on the homework assignments (65%) and final project (35%). Any homework turned in late will be penalized 25% of possible points. Please note that late final projects are not accepted.
Students not in full-year TICR Programs who satisfactorily pass all course requirements will receive a Certificate of Course Completion.
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 March 19, 2021. Only one application needs to be completed for all courses desired during the quarter.
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