BIOMEDE 5710
Transcript Abbreviation:
ML in BME
Course Description:
The goal of this course is to introduce ML and apply basic ML techniques to biomedical engineering applications. This course is intended for BME undergraduates and graduate students with limited exposure to ML basics.
Course Levels:
Undergraduate (1000-5000 level)
Graduate (5000-8000 level)
Designation:
Elective
General Education Course:
(N/A)
Cross-Listings:
(N/A)
Credit Hours (Minimum if “Range”selected):
3.00
Max Credit Hours:
(N/A)
Select if Repeatable:
Off
Maximum Repeatable Credits:
(N/A)
Total Completions Allowed:
(N/A)
Allow Multiple Enrollments in Term:
No
Course Length:
14 weeks (autumn or spring)
Off Campus:
Never
Campus Location:
Columbus
Instruction Modes:
In Person (75-100% campus; 0-24% online)
Prerequisites and Co-requisites:
Senior or graduate standing in BME or College of Medicine, or permission of instructor
Electronically Enforced:
Yes
Exclusions:
(N/A)
Course Goals / Objectives:
Recognize BME applications that can benefit from ML methods
Upload, manipulate, and display data from various biomedical applications
Understand NN basics, including loss function, activation function, and backpropagation
Formulate, implement, and apply supervised ML techniques, including classification, regression, and segmentation, to biomedical applications
Formulate, implement, and apply sequence modeling techniques to biomedical applications
Formulate, implement and apply clustering and dimensionality reduction techniques to biomedical applications
Recognize ethics of biomedical applications of ML and identify emerging paradigms
Check if concurrence sought:
Yes
Contact Hours:
| Topic | LEC | REC out-of-class | REC in-class | Weekly LAB out-of-class | Weekly LAB in-class |
|---|---|---|---|---|---|
| Introduction, terminology, setting up Anaconda | 2.5 | 0 | 0 | 0 | 0 |
| pandas, NumPy, scikit-learn | 2.5 | 0 | 0 | 0 | 0 |
| Linear and Multiple Regressions | 5 | 0 | 0 | 0 | 0 |
| Logistic Regression | 2.5 | 0 | 0 | 0 | 0 |
| Regression/Classification with NN | 5 | 0 | 0 | 0 | 0 |
| CNN and Image Processing | 5 | 0 | 0 | 0 | 0 |
| Sequence Modeling | 2.5 | 0 | 0 | 0 | 0 |
| RNN and LSTM | 5 | 0 | 0 | 0 | 0 |
| Unsupervised Learning | 5 | 0 | 0 | 0 | 0 |
| Ethics in ML and Advanced Topics | 2.5 | 0 | 0 | 0 | 0 |
| Total | 37.5 | 0 | 0 | 0 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Top Hat Quiz | 10% |
| Homework | 35% |
| Two Midterms | 30% |
| Final Project | 25% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| An Introduction to Statistical Learning | James, Witten, Hastie, and Tibshirani | |
| The Elements of Statistical Learning | Hastie, Tibshirani, and Friedman |
ABET-CAC Criterion 3 Outcomes:
(N/A)
ABET-ETAC Criterion 3 Outcomes:
(N/A)
ABET-EAC Criterion 3 Outcomes:
(N/A)
Embedded Literacies Info:
1.1 Investigate and integrate knowledge of the subject, context and audience with knowledge
1.1A Explain basic concepts of statistics and probability
1.4B Evaluate the social and ethical implications of data collection and analysis, especially in relation to human subjects
1.2 Recognize how technologies emerge and change