CAREER: Using Machine Learning to Understand and Enhance Human Learning Capacity

职业：利用机器学习来理解和增强人类的学习能力

• 批准号: 0953219
• 负责人: Xiaojin Zhu
• 金额: $ 46.56万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-06-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0953219&HistoricalAwards=false
• 关键词: CAREER; Using; Machine; Learning; Understand

Understanding and enhancing human learning are important challenges in the 21st century. Existing human category learning models cannot quantify important capacities such as people's (in)ability to generalize from training to test, to learn from imperfect data, or to learn by actively asking questions. This research project studies human learning using machine learning. It first develops machine learning theory and algorithms to quantify these human learning capacities: It establishes learning-theoretic error bounds on human generalization performance; It models human learning from an imperfect teacher with non-parametric Bayesian methods; It models human's ability to ask informative questions with active learning theory. The project then studies computational approaches to enhance human learning: It develops "machine teaching" algorithms when the computer knows the target concept, and selects the optimal training examples to teach a human learner; It develops "human machine co-learning" algorithms when the computer does not know the target concept, but instead learns alongside the human and suggests better learning strategies to her. Each topic is verified by human experiments.The project advances machine learning with new learning theory and algorithms on tasks where humans excel. It advances cognitive psychology with new models of human learning. It has broader impacts in understanding human intelligence, and in benefiting students with new educational tools. This research project is integrated with an educational plan that incorporates undergraduate and graduate teaching and mentoring, developing a new course and a book on machine and human learning, organizing seminars, tutorials and workshops, and sharing all results on a website.

理解和促进人类学习是21世纪世纪的重要挑战。 现有的人类类别学习模型无法量化重要的能力，例如人们从训练到测试的概括能力，从不完美的数据中学习的能力，或者通过主动提问来学习的能力。 该研究项目使用机器学习来研究人类学习。 它首先开发了机器学习理论和算法来量化这些人类学习能力：它建立了人类泛化性能的学习理论误差范围;它用非参数贝叶斯方法对人类从不完美的教师学习进行建模;它用主动学习理论对人类提出信息性问题的能力进行建模。 然后，该项目研究了增强人类学习的计算方法：当计算机知道目标概念时，它开发了“机器教学”算法，并选择最佳训练示例来教授人类学习者;当计算机不知道目标概念时，它开发了“人机协同学习”算法，而是与人类一起学习，并向她提出更好的学习策略。每个主题都经过人体实验验证。该项目通过新的学习理论和算法推进机器学习，人类出类拔萃 它用人类学习的新模型推进了认知心理学。 它在理解人类智力方面具有更广泛的影响，并使学生受益于新的教育工具。 该研究项目与一个教育计划相结合，该计划包括本科和研究生教学和指导，开发一门新课程和一本关于机器和人类学习的书，组织研讨会，教程和研讨会，并在网站上分享所有结果。