This course explores the foundational and applied aspects of machine learning techniques used to analyze image and time-series data, with a focus on healthcare applications. Learners will gain hands-on experience in designing models that detect brain tumors from MRI scans and predict clinical events such as sepsis onset using patient vital signs.
You’ll also gain exclusive insights from a now-retired, globally recognized pioneer in medical technology—whose decades-long career shaped the field and who now shares hard-earned wisdom to inspire and guide the next generation of innovators.
This course is ideal for:
• Healthcare professionals (e.g., clinicians, nurses, administrators) looking to understand how AI and machine learning can enhance patient care and operational efficiency.
• Data scientists and analysts working in or transitioning to the healthcare industry.
• Students and researchers in fields like biomedical engineering, public health, or health informatics who want a practical introduction to ML in clinical contexts.
• Healthcare innovators and tech entrepreneurs aiming to build or evaluate AI-driven healthcare solutions.
Overview
Syllabus
- Computer Vision: Images and the Shape of Their Data
- The first module explores images and the vital role their data structure plays in computer vision.
- Building Blocks of Computer Vision
- In the second module, we explore more building blocks of computer vision and begin working with real-life datasets.
- Sequence Analysis Part 1: Time Series
- This module introduces learners to time series analysis using real-world datasets focused on human activity.
- Sequence Analysis Part 2: State Transitions
- This module introduces advanced techniques for identifying state transitions in time series data.
Taught by
Ghaith Habboub, MD