The Robotics Major at the University of Michigan was successfully launched in
the 2022-23 academic year as an innovative step forward to better serve
students, our communities, and our society. Building on our guiding principle
of "Robotics with Respect" and our larger Robotics Pathways model, the Michigan
Robotics Major was designed to define robotics as a true academic discipline
with both equity and excellence as our highest priorities. Understanding that
talent is equally distributed but opportunity is not, the Michigan Robotics
Major has embraced an adaptable curriculum that is accessible through a
diversity of student pathways and enables successful and sustained career-long
participation in robotics, AI, and automation professions. The results after
our planning efforts (2019-22) and first academic year (2022-23) have been
highly encouraging: more than 100 students declared Robotics as their major,
completion of the Robotics major by our first two graduates, soaring
enrollments in our Robotics classes, thriving partnerships with Historically
Black Colleges and Universities. This document provides our original curricular
proposal for the Robotics Undergraduate Program at the University of Michigan,
submitted to the Michigan Association of State Universities in April 2022 and
approved in June 2022. The dissemination of our program design is in the spirit
of continued growth for higher education towards realizing equity and
excellence.
The most recent version of this document is also available on Google Docs
through this link: this https URL

密歇根大学的机器人学专业在 2022-23 学年成功启动，以实现机器人学作为一门真正的学术学科为目标，注重公平与卓越，通过多样化的学生途径和可持续的职业参与机器人、人工智能和自动化领域的课程，吸引 100 多名学生申报为机器人学专业，首两名毕业生完成了机器人学专业，机器人学课程的学生人数急剧增加，并与历史悠久的黑人学院和大学建立了良好的合作关系。

密歇根机器人学本科课程：为平等与优秀定义机器人学学科

The Michigan Robotics Undergraduate Curriculum: Defining the Discipline  of Robotics for Equity and Excellence

This paper presents the results of research on supervised extractive text
summarisation for scientific articles. We show that a simple sequential tagging
model based only on the text within a document achieves high results against a
simple classification model. Improvements can be achieved through additional
sentence-level features, though these were minimal. Through further analysis,
we show the potential of the sequential model relying on the structure of the
document depending on the academic discipline which the document is from.

通过研究，本文在科学文章的有监督摘要提取方面展现了基于仅使用文档内文本的简单序列标记模型对简单分类模型的高效结果，并对句子级特征进行了最小化改进。通过进一步分析，我们展示了序列模型依赖文档结构且取决于学科领域的潜力。