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Experience scope
Categories
Machine learning Artificial intelligence Data analysis Data modelling Data scienceSkills
automated machine learning machine learningThis experience is designed for learners with a foundational understanding of machine learning, with applications in any domain. Focused projects in biomedical computation, and health data analytics are also welcome. Participants will work in small teams or individually to apply their skills in coding and parallel computing to develop innovative AI solutions for real-world challenges in any domain, including in health care. The program aims to bridge the gap between theoretical knowledge and practical application, enabling learners to create impactful technologies with real-world application. Although I tend to focus on medical applications, I make extensive use of general purpose AI, and students choose projects from all topic domains.
Learners
- Prototype of a machine learning model for predicting anything of interest
- Technical report detailing the AI solution and its potential impact
- Presentation of project findings and recommendations
- Addition of novel features to AI models
Project timeline
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September 26, 2025Experience start
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September 30, 2025Phase 1
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October 21, 2025Phase 2
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November 18, 2025Phase 3
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December 20, 2025Experience end
Timeline
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September 26, 2025Experience start
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September 30, 2025Phase 1
Choose a dataset
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October 21, 2025Phase 2
Basic data cleaning and analysis
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November 18, 2025Phase 3
More advanced research
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December 20, 2025Experience end
Project examples
- Develop a machine learning model to predict hospital readmission rates
- Design an AI system for personalized medicine recommendations based on patient data
- Implement a parallel computing solution to accelerate AI analyses
- Build a predictive analytics tool for early detection of chronic diseases
- Create a machine learning algorithm to optimize resource allocation in hospitals
- Design a system for real-time monitoring and analysis of patient vitals
Our past project examples:
- Identifying Cortical Molecular Biomarkers Potentially Associated with Learning in Mice Using Artificial Intelligence
- Diagnosing and Characterizing Chronic Kidney Disease with Machine Learning: The Value of Clinical Patient Characteristics as Evidenced from an Open Dataset
- Mitigating Bias Due to Race and Gender in Machine Learning Predictions of Traffic Stop Outcomes
Additional company criteria
Companies must answer the following questions to submit a match request to this experience: