Computer Vision and Sequence Analysis in Machine Learning
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Computer Vision and Sequence Analysis in Machine Learning
Instructor: Ghaith Habboub, MD
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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.
Details to know
January 2026
4 assignments
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There are 4 modules in this course
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.
Youβll also gain exclusive insights from a now-retired, globally recognized pioneer in medical technologyβwhose 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: β’ Healthcare professionals (e.g., clinicians, nurses, administrators) looking to understand how AI and machine learning can enhance patient care and operational efficiency. β’ Data scientists and analysts working in or transitioning to the healthcare industry. β’ Students and researchers in fields like biomedical engineering, public health, or health informatics who want a practical introduction to ML in clinical contexts. β’ Healthcare innovators and tech entrepreneurs aiming to build or evaluate AI-driven healthcare solutions.
The first module explores images and the vital role their data structure plays in computer vision.
What's included
3 videos1 reading1 assignment
3 videosβ’Total 17 minutes
- What Sets Images Apartβ’6 minutes
- Shape of Dataβ’4 minutes
- Journey Through 50 Years of Changing Technologyβ’7 minutes
1 readingβ’Total 10 minutes
- Course Syllabusβ’10 minutes
1 assignmentβ’Total 30 minutes
- Module 1 Assessmentβ’30 minutes
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
6 videosβ’Total 29 minutes
- Convolutional Neural Networkβ’5 minutes
- Computer Vision Refresher and a Working CNN Exampleβ’7 minutes
- CNN Working Example, Part 2β’6 minutes
- Defining Transfer Learningβ’2 minutes
- A Transfer Learning Exampleβ’4 minutes
- Segmentation and Interpretabilityβ’5 minutes
1 assignmentβ’Total 45 minutes
- Module 2 Assessmentβ’45 minutes
2 programming assignmentsβ’Total 360 minutes
- Computer Vision CNNβ’180 minutes
- Computer Vision Transfer Learningβ’180 minutes
This module introduces learners to time series analysis using real-world datasets focused on human activity.
What's included
4 videos1 assignment1 programming assignment
4 videosβ’Total 21 minutes
- Time Series Analysisβ’6 minutes
- 1D Convolutional and Recurrent Neural Networksβ’3 minutes
- Time Series Example Part 1: Human Activity Datasetβ’6 minutes
- Time Series Example Part 2: Human Activity Datasetβ’5 minutes
1 assignmentβ’Total 30 minutes
- Module 3 Assessmentβ’30 minutes
1 programming assignmentβ’Total 180 minutes
- Time Series Human Activityβ’180 minutes
This module introduces advanced techniques for identifying state transitions in time series data.
What's included
5 videos1 assignment1 programming assignment
5 videosβ’Total 20 minutes
- State Transition in Time Seriesβ’3 minutes
- Robust Principal Component Analysisβ’2 minutes
- Enduring Power of Certain Technologiesβ’5 minutes
- Preparing for the Upcoming Applied Sequence Analysisβ’2 minutes
- Sequence Analysis Datasetβ’7 minutes
1 assignmentβ’Total 30 minutes
- Module 4 Assessmentβ’30 minutes
1 programming assignmentβ’Total 180 minutes
- RPCA and Hidden Markovβ’180 minutes
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