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In diesem Kurs gibt es 3 Module
"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.
Das ist alles enthalten
2 Videos5 Lektüren1 Aufgabe1 Unbewertetes Labor
Infos zu Modulinhalt anzeigen
2 Videos•Insgesamt 32 Minuten
AI for Energy Generation and Distribution •15 Minuten
AI for Energy Storage and Consumption •18 Minuten
5 Lektüren•Insgesamt 50 Minuten
Course Syllabus•10 Minuten
Help Us Learn About You!•10 Minuten
Introduction to Jupyter Labs on Coursera•10 Minuten
Random Forest•10 Minuten
Random Forest Implementation•10 Minuten
1 Aufgabe•Insgesamt 30 Minuten
Module 1 Assignment•30 Minuten
1 Unbewertetes Labor•Insgesamt 60 Minuten
Random Forest- Programming Exercise•60 Minuten
Predictive Maintenance for Energy Infrastructure
Modul 2•2 Stunden abzuschließen
Moduldetails
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.
Das ist alles enthalten
2 Videos2 Lektüren1 Aufgabe1 Unbewertetes Labor
Infos zu Modulinhalt anzeigen
2 Videos•Insgesamt 28 Minuten
AI for Predictive Maintenance in Power Generation & Grid Systems•13 Minuten
AI-powered Predictive Maintenance for Renewable Energy & Storage Systems•15 Minuten
2 Lektüren•Insgesamt 20 Minuten
Handling Abnormal Data in Energy Applications•10 Minuten
Autoencoder-Decoder Models for Abnormal Data in Energy Systems Implementation•10 Minuten
1 Aufgabe•Insgesamt 30 Minuten
Module 2 Assignment•30 Minuten
1 Unbewertetes Labor•Insgesamt 60 Minuten
Auto Encoder-Decoder Models for Abnormal Data in Energy Systems- Programming Exercise•60 Minuten
AI in Medical Imaging, Genomics, and Drug Discovery
Modul 3•2 Stunden abzuschließen
Moduldetails
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.
Das ist alles enthalten
2 Videos4 Lektüren1 Aufgabe
Infos zu Modulinhalt anzeigen
2 Videos•Insgesamt 29 Minuten
AI in Medical Image and Genomic Data Interpretation•15 Minuten
AI-Driven Drug Discovery and Genomic Insights for Personalized Medicine•14 Minuten
4 Lektüren•Insgesamt 40 Minuten
Applications of Convolutional Neural Networks in Biomedical Fields•10 Minuten
End of Course Survey•10 Minuten
References•10 Minuten
Continue your AI education with the AI Collection from Michigan Online•10 Minuten
1 Aufgabe•Insgesamt 30 Minuten
Module 3 Assignment•30 Minuten
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