AI for Energy and Biomedical Applications
This course is part of AI for Mechanical Engineers Specialization.
This comprehensive course explores the revolutionary applications of AI in energy systems and biomedical fields. In the energy sector, you'll learn cutting-edge techniques for optimizing renewable energy production, enhancing energy efficiency, and improving grid management. The course covers essential AI-driven approaches including predictive maintenance, demand forecasting, and energy storage optimization to address pressing sustainability challenges. In biomedical applications, you'll discover how AI is transforming disease diagnosis, drug discovery, and personalized medicine through advanced medical image analysis, genomic data interpretation, and predictive modeling. By mastering these AI applications, you'll be equipped to drive innovation in both energy systems and healthcare, developing solutions that enhance sustainability and improve patient outcomes.
Instructors:
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What you'll learn
Develop AI-driven techniques for energy optimization and management
Implement predictive maintenance models for energy infrastructure
Master AI applications for renewable energy integration
Apply machine learning to analyze medical images
Interpret genomic data using AI algorithms
Understand AI's role in drug discovery and personalized medicine
Skills you'll gain
This course includes:
1.5 Hours PreRecorded video
3 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 3 modules in this course
This course bridges the gap between AI technology and its practical applications in energy systems and biomedical fields. Students will learn how AI is revolutionizing energy management through optimization techniques for generation, distribution, and storage systems. The course explores predictive maintenance strategies for energy infrastructure using machine learning and anomaly detection. In the biomedical realm, students will discover how AI transforms medical imaging, genomic data interpretation, and drug discovery processes. Through programming exercises using techniques like Random Forests and Autoencoder-Decoder models, participants gain hands-on experience solving real-world problems in both domains.
AI in Energy Systems
Module 1 · 2 Hours to complete
Predictive Maintenance for Energy Infrastructure
Module 2 · 2 Hours to complete
AI in Medical Imaging, Genomics, and Drug Discovery
Module 3 · 1 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: AI for Mechanical Engineers Specialization
Instructor
Professor of Mechanical Engineering
Wei Lu is a Professor in the Department of Mechanical Engineering at the University of Michigan - Ann Arbor. He earned his B.S. from Tsinghua University and his Ph.D. from Princeton University. Prof. Lu specializes in applying machine learning to solve critical challenges in mechanical engineering and energy applications. With over 180 publications in high-impact journals and 200 presentations at prestigious institutions like Harvard, MIT, and Stanford, he is a recognized leader in his field. Prof. Lu's research spans a wide range of topics, integrating artificial intelligence with engineering solutions. His works have been featured in prominent journals such as Nature Communications, Applied Energy, and Journal of Power Sources, showcasing his contributions to both specialized and interdisciplinary audiences.
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