The following directory lists the graduate courses which the University expects to offer, although the University in no way guarantees that all such courses will be offered in any given academic year, and reserves the right to alter the list if conditions warrant. Click on the links below for a list of courses in that subject area. You may then click “View Classes” to see scheduled classes for individual courses.
6810. Machine Learning Methods for Biomedical Signal Analysis
3.00 credits
Prerequisites: Instructor consent; CSE 1010 and STAT 3025Q or equivalent. Not open for credit to students who have passed BME 4810.
Grading Basis: Graded
Acquire the basic machine learning concepts and tools that are necessary in modern biomedical engineering to model, analyze, and classify physiological time series. Specific focus is on multivariate data and time series extracted from multiple physiological sources, including (but not limited to) ECG, EEG, and EMG. Through a mix of lectures and hands-on laboratory experiences, the students will learn how to design and implement machine learning projects and how to use advanced statistical tools and methods to classify data, infer predictions, and validate data-driven predictive models.
Last Refreshed: 16-APR-24 05.20.16.558219 AM
| Term | Class Number | Campus | Instruction Mode | Instructor | Section | Session | Schedule | Enrollment | Location | Credits | Grading Basis | Notes | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1243 4624 1 010X | Spring 2024 | 4624 | Storrs | Online Synchronous | Santaniello, Sabato | 010X | Reg | Tu 5:00pm‑8:00pm |
0/10 | No Room Required - Online | 3.00 | Graded | |
| 1243 4625 1 017 | Spring 2024 | 4625 | Storrs | In Person | Santaniello, Sabato | 017 | Reg | Tu 5:00pm‑8:00pm |
8/10 | E2 305 | 3.00 | Graded |