Case studies to explore specific examples in depth

Case studies to explore specific examples in depth

Instructions

 Case studies to explore specific examples in depth

The use of machine learning in healthcare: One example of a machine learning application in healthcare is using it to predict patient outcomes. Researchers at the University of California, San Francisco trained a machine learning algorithm on a dataset of over 200,000 patients to predict the likelihood of a patient being readmitted to the hospital within 30 days of their discharge. The algorithm was able to predict readmissions with an accuracy of 82%, which is higher than the accuracy of traditional methods. This type of application could help hospitals and healthcare providers to identify high-risk patients and take steps to prevent readmissions, ultimately improving patient outcomes and reducing healthcare costs.

The use of machine learning in finance: Machine learning is increasingly being used in the finance industry to improve risk management and detect fraud. For example, JPMorgan Chase uses machine learning algorithms to monitor transactions and detect suspicious activity. The bank reported that the system was able to identify and prevent $600 million of fraudulent transactions in one year. Additionally, Goldman Sachs has developed a machine learning-based system that can analyze vast amounts of data and help traders make better decisions.

The use of machine learning in retail: Machine learning is also being used in the retail industry to personalize the shopping experience for customers and improve inventory management. For example, Amazon uses machine learning algorithms to recommend products to customers based on their browsing and purchase history. The company also uses machine learning to optimize its inventory management and predict demand for products. Another example is Walmart, the company has implemented machine learning to optimize pricing and product assortment in their stores.

The use of machine learning in transportation: Machine learning is being used in transportation to optimize routes, reduce fuel consumption, and improve overall efficiency. For example, UPS has implemented machine learning algorithms to optimize delivery routes, which has helped the company to reduce fuel consumption by 10%. Additionally, General Electric has developed a machine learning-based system for airplane engines that can predict when maintenance is needed, which helps to reduce downtime and increase efficiency.

The use of machine learning in education: Machine learning is being used in education to personalize learning for students, improve the student-teacher interaction and automate the grading process. For example, a company called Knewton uses machine learning algorithms to personalize the learning experience for students by adapting to their individual strengths and weaknesses. Additionally, many universities and colleges are using machine learning to grade student essays and assignments, which reduces the workload for teachers and allows them to provide more detailed feedback to students.

These are just a few examples of the many ways that machine learning is being used in different industries. As the technology continues to improve and become more widely adopted, it is likely that we will see even more innovative uses of machine learning in the future.

Case studies to explore specific examples in depth

RUBRIC
Excellent Quality 95-100%	Introduction 45-41 points The background and significance of the problem and a clear statement of the research purpose is provided. The search history is mentioned.		Literature Support 91-84  points The background and significance of the problem and a clear statement of the research purpose is provided. The search history is mentioned.			Methodology 58-53 points Content is well-organized with headings for each slide and bulleted lists to group related material as needed. Use of font, color, graphics, effects, etc. to enhance readability and presentation content is excellent. Length requirements of 10 slides/pages or less is met.
Average Score 50-85%	40-38 points More depth/detail for the background and significance is needed, or the research detail is not clear. No search history information is provided.		83-76  points Review of relevant theoretical literature is evident, but there is little integration of studies into concepts related to problem. Review is partially focused and organized. Supporting and opposing research are included. Summary of information presented is included. Conclusion may not contain a biblical integration.			52-49  points Content is somewhat organized, but no structure is apparent. The use of font, color, graphics, effects, etc. is occasionally detracting to the presentation content. Length requirements may not be met.
Poor Quality 0-45%	37-1 points The background and/or significance are missing. No search history information is provided.		75-1 points Review of relevant theoretical literature is evident, but there is no integration of studies into concepts related to problem. Review is partially focused and organized. Supporting and opposing research are not included in the summary of information presented. Conclusion does not contain a biblical integration.			48-1 points There is no clear or logical organizational structure. No logical sequence is apparent. The use of font, color, graphics, effects etc. is often detracting to the presentation content. Length requirements may not be met
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