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.
Computer Vision and Sequence Analysis in Machine Learning
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Computer Vision and Sequence Analysis in Machine Learning
Instructor: Ghaith Habboub, MD
Included with
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Analyze the unique structure and dimensionality of image data compared to tabular data.
Build and optimize convolutional neural networks (CNNs) for medical image classification and segmentation.
Apply transfer learning to improve model performance on limited datasets.
Skills you'll gain
Details to know
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Recently updated!
January 2026
Assessments
4 assignments
Taught in English
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There are 4 modules in this course
The first module explores images and the vital role their data structure plays in computer vision.
What's included
3 videos1 reading1 assignment
In the second module, we explore more building blocks of computer vision and begin working with real-life datasets.
What's included
6 videos1 assignment2 programming assignments
This module introduces learners to time series analysis using real-world datasets focused on human activity.
What's included
4 videos1 assignment1 programming assignment
This module introduces advanced techniques for identifying state transitions in time series data.
What's included
5 videos1 assignment1 programming assignment
Instructor
Offered by
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