时 间： 2020年7月12日 上午 8:30-11:30 (I)
2020年7月13日 上午 8:30-11:30 (II)
报告人： David Banks
主持人： 周迎春 教授
摘 要：
This course is an introduction to deep learning,but taught from a statistical perspective. It will focus on convolutional neural networks, recurrent neuralnetworks, and some of their competitors. It will discuss the bias-variance tradeoff, stochastic gradient descent,long short-term memory, and related topics. The course is self-contained and has no prerequisites, but a good mathematical and statistical background will be helpful.
个人简介：
David Banks was the coordinating editor of the Journal of the American Statistical Association. He led SAMSI research program, on Computational Advertising. He has published 74 refereed articles, edited eight books, and written four monographs. His research areas include models for dynamic networks, dynamic text networks,adversarial risk analysis (i.e., Bayesian behavioral game theory), human rights statistics, agent-based models, forensics, and certain topics in high-dimensional data analysis.
David Banks is past-president of the Classification Society, and has twice served on the Board of Directors of the American Statistical Association. He is currently the president of the International Society for Business and Industrial Statistics.He is a fellow of the American Statistical Association and of the Institute of Mathematical Statistics. He recently won the American Statistical Association's Founders Award.