Graduate Course Descriptions

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: 08-JUN-23 AM
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Term Class Number Campus Instruction Mode Instructor Section Session Schedule Enrollment Location Credits Grading Basis Notes
Spring 2023 9979 Storrs Distance Learning Santaniello, Sabato 010X Reg We 5:00pm‑8:00pm
1/10 No Room Required - Online 3.00 Graded
Spring 2023 9669 Storrs In Person Santaniello, Sabato 017 Reg We 5:00pm‑8:00pm
7/10 E2 305 3.00 Graded