学术报告
Application of statistical techniques in the field of Big Data
题目: Application of statistical techniques in the field of Big Data
报告人: 蒋建成
Course Description: This course provides students a survey of major statistical learning methods and concepts for both supervised and unsupervised learning including resampling methods, support vector machines, model selection and regularization, tree-based methods and ensembles, statistical graphics. Students learn how and when to apply statistical learning techniques, their comparative strengths and weaknesses, and how to critically evaluate the performance of learning algorithms in case studies in financial investment, gene identification, and feature selection in high-dimensional spaces. Software R will be used for simulations and for real data analysis.
Course Objectives:By providing students a survey of major statistical learning methods, the course is designed for students to learn how and when to apply statistical learning techniques, how to critically evaluate the performance of learning algorithms, and how to build predictive models. Students completing this course should be able to apply basic statistical learning methods to build predictive models, to properly tune and select statistical learning models, to correctly assess model fit and error, and to build an ensemble of learning algorithms for data.
时间：2016年7月11日（周一）—2016年7月17日（周日）8:00-12:00
地点：首都师大北一区文科楼709教室
欢迎教师和研究生积极参加！
