授课教师：Alexander Stanoyevitch

国籍：美国

职称：教授

教师简介（中英文）：

Alexander Stanoyevitch现为加州州立大学数学系教授，1983在马里兰大学获学士学位，1986年和1990年在密歇根大学分别获得数学与应用数学硕士和博士学位。出版专著四本，发表SCI高水平论文20多篇。

Alexander Stanoyevitch is professor at California State University,Dominguez Hills. Majored in Mathematics and Applied Mathematics, he got his bachelor's degree from Maryland University in 1983, master's degree and doctor's degree from Michigan University in 1986 and 1990, respectively. Professor Stanyevitch authored 4 books and published more than 20 high level scientific papers.

课程简介（中英文）：

本课程将介绍机器学习中的监督学习（通过决策树）和无监督学习（通过k-means和层次聚类）的概念和方法，除了介绍相应的理论基础，还将重点分析把机器学习的算法应用于数据集的主要方法。主要实现的软件工具为R。

The course will introduce the concepts and methods of machine learning in the two main areas: supervised learning (via decision trees) and unsupervised learning (via k-means and hierarchical clustering). We will cover both the theoretical underpinnings as well as the main ways to apply any machine learning algorithm to a data set. The software R will be used for implementations.