"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.
AI for Energy and Biomedical Applications
This course is part of AI for Mechanical Engineers Specialization
Instructor: Wei Lu
Sponsored by Louisiana Workforce Commission
Recommended experience
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
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3 assignments
December 2024
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There are 3 modules in this course
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
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
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 videos3 readings1 assignment
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