Theoretical Aspects of Machine Learning and Artificial Intelligence

PI计划研究可证明有效的机器技术， 学习人工智能的理论方面。 要研究的一些主题是：学习未知的技术 系统或环境的勘探，技术代表和 利用先验知识，交易假设复杂性的技术 为了适应数据，学习确定性系统， 有限状态概率系统的学习 概率转换或输出函数。

The PI plans to work on provably effective techniques for machine learning in the area of theoretical aspects of artificial intelligence. Some topics to be studied are: techniques for learning an unknown system or environment by exploration, techniques for representing and utilizing prior knowledge, techniques for trading hypothesis complexity for fit to the data, learning deterministic systems with an infinite number of states and learning finite-state probabilistic systems with probabilistic transitions or output functions.