This article is produced by NetEase Smart Studio (public number smartman 163). Focus on AI and read the next big era! [Netease smart news November 17 news] Who would have thought that the story of self-driving cars will actually come true, even machine learning algorithms can drive the computer to communicate with humans, driving cars, playing games, but also can do things that humans can not do . Mathematical algorithm-driven machine learning and scientific innovation have become an important part of our lives. For example, Google applies a probabilistic algorithm to automatically correct misspelled words. This action applies the principle of machine learning. Machine learning compares the search database made up of the accumulation of millions of other users to predict the words we are going to use. With the continuous development of science and technology, machine learning will soon become a new opportunity for higher education, and it is expected to be able to personalize education from all levels. It reads and recognizes data patterns, and proposes algorithms that can perform data-driven prediction and decision making. The more data we enter in a computer, the smarter the algorithm becomes, and can make full use of all areas of statistical pattern recognition. Today, machine learning plays a crucial role in the field of education and thus improves the quality of formal and non-formal courses in many ways. Educators have witnessed this. With the integration of machine learning, education and teaching efficiency have improved, while providing teachers and students with a customizable learning experience. Therefore, higher education institutions should be fully equipped to learn the full potential of excavator machines and achieve success at all levels. Identity and permissions issues As people become increasingly concerned about privacy issues, they believe that data is the key to personalized education, and that such visits should be based on identity differences and call for policy makers to focus on personalization and privacy issues. Machine learning can help organizations use logic analysis to identify users' personal data and provide them with the required access rights, allowing them to log in to different systems according to their level. To prevent suspicious behavior or unauthorized access, Machine Learning triggers additional conditions during the authentication process to ensure that no network attacks occur. Custom workflow management For higher education institutions, it is very important to improve students' academic success rate and management efficiency by analyzing past data. Machine learning, by studying student types to understand the level of risk, helps to establish predictive models of motivation, helps organizations to reduce staff turnover, and improves student retention. It can also help organizations use algorithms to learn to analyze historical data, predict them to solve problems, and make it easier to categorize requests and route issues. Make informed decisions Data analysis can help organizations unlock historical data and help them answer real strategic questions to make informed decisions. Based on these data, the organization can evaluate the risks, results, and costs involved in building a new process and make decisions to drive user engagement. Machine learning provides organizations with intuitive business intelligence dashboards to track trends, key performance indicators, and improve student retention. Evaluate student performance Organizations should analyze and evaluate student performance to help them improve and provide them with a better learning environment. Machine learning can be assessed based on the student’s current academic record to predict the future development of a student. Historical data can help organizations analyze and monitor students’ progress. This also reminds students to solve any problems and challenges they may face, or to ensure that they can get all the help they need. Admission Management For students, the entire admission process may be very depressing. From the day they apply for college to the day they sign up for classes, machine learning can study student behavior. Research includes analyzing the logic behind the choices they make. University institutions mainly use two kinds of machine learning prediction algorithms, namely linear regression and logistic regression, to record some behaviors in the course of admission according to the students' academic achievements and reviews, thus making the admission process of the whole university seamless. Education develops faster than ever before. In order to enable students to grow together, the education industry needs to provide a sound environment for growth. Machine learning can help organizations use learning analytics and algorithms to build statistical models of student knowledge. Higher education is the time to get involved in machine learning as an important business function and to transform its business output model. (Source: Yourstory Compilation: NetEase Execution Translation Robot: Sarah) Pay attention to NetEase smart public number (smartman163), obtain the latest report of artificial intelligence industry.
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