【12.28腾讯会议】Machine Learning Axioms and Applications

报告题目: Machine Learning Axioms and Applications

报告学长: Jian Yu(于剑) 


报告地点:腾讯会议 ID: 381 6014 5369

摘要:Up to now, many learning algorithms have been presented and successfully applied in many tasks. Yes, some learning theories have been proposed, such as PAC theory, Statistical learning theory and Bayesian theory.  However, it is an open problem what are the fundamental statistical-computational-information-theoretic laws that govern all learning systems, including computers, humans, and organizations. In this talk, I will solve this problem by presenting a novel framework of axiomizing machine learning. With this framework, density estimation, regression, dimension reduction, clustering and classification can be represented and inferred, even more, some soft clustering algorithms can be analyzed.

报告人简介:Jian Yu, professor in School of Computer and Information Technology, Director of Institute of AI, Chief of Beijing Key Lab of Transportation Data Analysis and Mining, Beijing Jiaotong University.

His major interests including artificial intelligence, machine learning, natural language processing, etc. He has published a book entitled “Machin learning: from axioms to algorithms”.