Professor of Mechanical Engineering
AI for Energy and Biomedical Applications
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
3 Modules
6 Hours
2 hrs per module (approx.)
Rating
About AI for Energy and Biomedical Applications
"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.
Skills You'll Gain
- Generative AI Agents
- Healthcare Quality
- Predictive Maintenance
- Sustainability Procedures
What You'll Earn
- Certificate of Completion:
- Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.
Welcome Message
Welcome to AI for Energy and Biomedical Applications, a course examining how AI transforms energy systems and healthcare technologies. You will explore predictive maintenance, energy optimization, medical image analysis, and personalized medicine applications. The course highlights how AI-driven models address sustainability, efficiency, and innovation across energy and biomedical domains.
This abbreviated syllabus description was created with the help of AI tools and reviewed by staff. The full syllabus is available to those who enroll in the course.
Course Schedule
Module 1: AI in Energy Systems
- Video: AI for Energy Generation and Distribution
- Reading: Course Syllabus
- Reading: Help Us Learn About You!
- Reading: Introduction to Jupyter Labs on Coursera
- Reading: Random Forest
- Video: AI for Energy Storage and Consumption
- Reading: Random Forest Implementation
- Ungraded Lab: Random Forest- Programming Exercise
- Graded: Module 1 Assignment
Module 2: Predictive Maintenance for Energy Infrastructure
- Video: AI for Predictive Maintenance in Power Generation & Grid Systems
- Reading: Handling Abnormal Data in Energy Applications
- Video: AI-powered Predictive Maintenance for Renewable Energy & Storage Systems
- Reading: Autoencoder-Decoder Models for Abnormal Data in Energy Systems Implementation
- Ungraded Lab: Auto Encoder-Decoder Models for Abnormal Data in Energy Systems- Programming Exercise
- Graded: Module 2 Assignment
Module 3: AI in Medical Imaging, Genomics, and Drug Discovery
- Video: AI in Medical Image and Genomic Data Interpretation
- Reading: Applications of Convolutional Neural Networks in Biomedical Fields
- Video: AI-Driven Drug Discovery and Genomic Insights for Personalized Medicine
- Reading: End of Course Survey
- Reading: References
- Reading: Continue your AI education with the AI Collection from Michigan Online
- Graded: Module 3 Assignment
Grading Policy
Each module includes a graded, equally weighted assignment, and learners must earn 80% or higher on each assignment to pass.
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Intermediate Level
Learners with general college-level education, industry-relevant experience, and professionals interested in the field are welcome.
