BME366_Spring2024_Syllabus.pdf
2024-01-12_BME 366 Final Project Rubric.pdf
This course presents foundational data science methods to analyze complex biological datasets encountered in biomedical engineering research and applications. After a brief (1-2 weeks) introduction of biomolecules and a one-week review of mathematical concepts necessary for data science, the course will cover representative areas of regression, supervised machine learning, unsupervised machine learning, model evaluation, and uncertainty quantification. Assignments and exams will focus on practical examples and theory spanning basic science, engineering, and medical applications.
Lecture: 12:30-1:20 pm, M/W/F Location: MJIS 1097 Office hours: Weds 12:30 - 3:00 pm Prof. Green Office: MJIS 3021
Leopold N. Green, PhD Email: greenln@purdue.edu
Jee Hyun Park Email: park1102@purdue.edu
Kunlun Wang Email: wang4736@purdue.edu
Homework (computation): 20% Class Participation (pop corn): 20% Journal Reflections: 10% Project Pitch: 10% Data Analysis Plan: 10% Final Presentation: 10% Final Report: 20%
A 90%-100% B 80%-89% C 70%-79% D 60%-69% F < 60%