Courses / Fundamentals of Biomedical ML
Module 4 of 7
Fundamentals of Biomedical ML
Develop practical machine learning skills tailored to biomedical science. This module covers supervised and unsupervised learning, feature engineering, deep learning architectures, and choosing the right models for specific biomedical tasks. Learners practice evaluating models using accuracy, sensitivity, specificity, calibration, and explainability tools such as SHAP and LIME.
Free
Format · Online
Start date · Aug 31, 2026
Duration · 2 weeks
Time · 5 hrs / week
Microskills · 7
CME credits · 11
What you will gain
Course Outcomes
Integration of AI techniques
Integrate AI and ML into healthcare research and clinical practice.
Real-world data application
Use real medical data and case studies to address healthcare challenges.
Practical AI skills
Work with code-free AI/ML tools — no programming knowledge needed.
Critical thinking
Analyze complex healthcare problems through AI-driven insights and methodologies.
What you will learn
Microskills in this Module
Each module covers 5 AI microskills plus one Scientific Rigor and Reproducibility (SRR) and one Responsible Conduct of Research (RCR) microskill.
- Shared biomedical AI vocabulary
- Applied fundamentals of ML and deep learning
- Choosing the right biomedical machine learning model
- Choosing the right biomedical deep learning model
- Evaluating biomedical machine learning models
- Model generalizability
- Ethics of black-box algorithms
Meet your instructors
Expert Faculty from the University of Florida
Azra Bihorac MD, MS
Senior Associate Dean for Research Affairs · Director, IC3
Guoshuai Cai, PhD
Assistant Director · Director of Surgery, Genomics Core
Feifei Xiao, PhD
Associate Professor
Tezcan Ozrazgat Baslanti, PhD
Research Associate Professor
Zhe He, PhD
Professor & Director for the Institute for Successful Longevity
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Ready to enroll?
Free enrollment. No coding required. Two cohorts per year — Spring and Fall.