Computer Vision and Sequence Analysis in ML

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

• Analyze the structure and dimensionality of image data compared to tabular data
• Build and optimize CNNs for medical image classification and segmentation
• Apply transfer learning to enhance model performance on limited datasets

• Basic familiarity with the topic and relevant terminology
• Willingness to engage in practical exercises or case studies