AI for Energy and Biomedical Applications
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AI for Energy and Biomedical Applications
This course is part of AI for Mechanical Engineers Specialization
4,529 already enrolled
Included with
What you'll learn
Gain proficiency with AI techniques for energy optimization
Develop an understanding of AI applications in biomedical sciences
Experiment with AI approaches to address energy and biomedical problems
Skills you'll gain
- Artificial Intelligence
- Predictive Modeling
- Machine Learning Algorithms
- Applied Machine Learning
- Anomaly Detection
- Electric Power Systems
- Sustainable Technologies
- Drug Development
- Bioinformatics
- Machine Learning Methods
- Medical Imaging
- Biomedical Engineering
- Energy and Utilities
- Precision Medicine
- Patient-centered Care
- Image Analysis
Tools you'll learn
Details to know
3 assignments
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There are 3 modules in this course
"AI for Energy and Biomedical Applications” explores the groundbreaking applications of AI technologies revolutionizing energy systems and advancing healthcare solutions. In the energy sector, AI is reshaping how we generate, distribute, and manage energy resources. From optimizing renewable energy production to enhancing energy efficiency and grid management, AI offers unprecedented opportunities for sustainability and resilience. Through this course, you will explore AI-driven techniques such as predictive maintenance, demand forecasting, and energy storage optimization, empowering you to drive innovation and address pressing energy challenges. In the realm of biomedical applications, AI is driving breakthroughs in disease diagnosis, drug discovery, and personalized medicine. You’ll delve into AI-driven approaches to medical image analysis, genomic data interpretation, and predictive modeling of disease progression. You’ll also gain insights into how AI is revolutionizing healthcare delivery, enabling early detection of diseases, and facilitating precision medicine tailored to individual patients.
In module 1 we will review challenges that we may face energy optimization. Then, we will explain different AI-driven energy optimization techniques including demand forecasting, load management, and renewable energy integration. Finally, we will examine AI-driven optimization strategies for energy storage systems.
What's included
2 videos5 readings1 assignment1 ungraded lab
2 videos•Total 32 minutes
- AI for Energy Generation and Distribution •15 minutes
- AI for Energy Storage and Consumption •18 minutes
5 readings•Total 50 minutes
- Course Syllabus•10 minutes
- Help Us Learn About You!•10 minutes
- Introduction to Jupyter Labs on Coursera•10 minutes
- Random Forest•10 minutes
- Random Forest Implementation•10 minutes
1 assignment•Total 30 minutes
- Module 1 Assignment•30 minutes
1 ungraded lab•Total 60 minutes
- Random Forest- Programming Exercise•60 minutes
In module 2, we explain predictive maintenance principles and continue to review AI driven predictive maintenance techniques including machine learning, deep learning, and anomaly detection algorithms. We explain how predictive maintenance models can be trained and optimized. Finally, we discuss strategies for integrating AI-driven predictive maintenance models into existing energy infrastructure systems.
What's included
2 videos2 readings1 assignment1 ungraded lab
2 videos•Total 28 minutes
- AI for Predictive Maintenance in Power Generation & Grid Systems•13 minutes
- AI-powered Predictive Maintenance for Renewable Energy & Storage Systems•15 minutes
2 readings•Total 20 minutes
- Handling Abnormal Data in Energy Applications•10 minutes
- Autoencoder-Decoder Models for Abnormal Data in Energy Systems Implementation•10 minutes
1 assignment•Total 30 minutes
- Module 2 Assignment•30 minutes
1 ungraded lab•Total 60 minutes
- Auto Encoder-Decoder Models for Abnormal Data in Energy Systems- Programming Exercise•60 minutes
In module 3, we review how AI techniques are used to analyze medical images and to interpret genomic data. We will discuss how AI has impacted drug discovery and other biomedical applications.
What's included
2 videos4 readings1 assignment
2 videos•Total 29 minutes
- AI in Medical Image and Genomic Data Interpretation•15 minutes
- AI-Driven Drug Discovery and Genomic Insights for Personalized Medicine•14 minutes
4 readings•Total 40 minutes
- Applications of Convolutional Neural Networks in Biomedical Fields•10 minutes
- End of Course Survey•10 minutes
- References•10 minutes
- Continue your AI education with the AI Collection from Michigan Online•10 minutes
1 assignment•Total 30 minutes
- Module 3 Assignment•30 minutes
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Reviewed on Oct 23, 2025
very valuable and important information.
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