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AI for Energy and Biomedical Applications

AI for Energy and Biomedical Applications

This course is part of AI for Mechanical Engineers Specialization

Instructor: Wei Lu

4,190 already enrolled

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

76 reviews

Intermediate level

Recommended experience

7 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

76 reviews

Intermediate level

Recommended experience

7 hours to complete

Flexible schedule

Learn at your own pace

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

Tools you'll learn

Generative AI

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

3 assignments

Taught in English

91% of learners achieved a positive career outcome

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Build your subject-matter expertise

This course is part of the AI for Mechanical Engineers Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

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.

Module details

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 videosTotal 32 minutes

AI for Energy Generation and Distribution 15 minutes
AI for Energy Storage and Consumption 18 minutes

5 readingsTotal 50 minutes

Course Syllabus10 minutes
Help Us Learn About You!10 minutes
Introduction to Jupyter Labs on Coursera10 minutes
Random Forest10 minutes
Random Forest Implementation10 minutes

1 assignmentTotal 30 minutes

Module 1 Assignment30 minutes

1 ungraded labTotal 60 minutes

Random Forest- Programming Exercise60 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 videosTotal 28 minutes

AI for Predictive Maintenance in Power Generation & Grid Systems13 minutes
AI-powered Predictive Maintenance for Renewable Energy & Storage Systems15 minutes

2 readingsTotal 20 minutes

Handling Abnormal Data in Energy Applications10 minutes
Autoencoder-Decoder Models for Abnormal Data in Energy Systems Implementation10 minutes

1 assignmentTotal 30 minutes

Module 2 Assignment30 minutes

1 ungraded labTotal 60 minutes

Auto Encoder-Decoder Models for Abnormal Data in Energy Systems- Programming Exercise60 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 videosTotal 29 minutes

AI in Medical Image and Genomic Data Interpretation15 minutes
AI-Driven Drug Discovery and Genomic Insights for Personalized Medicine14 minutes

4 readingsTotal 40 minutes

Applications of Convolutional Neural Networks in Biomedical Fields10 minutes
End of Course Survey10 minutes
References10 minutes
Continue your AI education with the AI Collection from Michigan Online10 minutes