课程号：

EE196002

课程名：

机器学习

英文课程名：

Machine Learning

开课单位：

电子工程学院

授课教师：

John Paisley

责任教师：

周峰

助理教师：

刘磊

学分/学时：

1/16

选课年级：

不限

选课专业：

电子信息类专业

教师简介（中英文）：

John Paisely，美国哥伦比亚大学电子工程系和数据科学研究所的副教授。2010年获得杜克大学电气与计算机工程博士学位，2010.01-2013.07分别在普林斯顿大学计算机科学系和加州大学伯克利分校EESC计算机科学部从事博士后研究，合作导师分别为David Blei和美国科学院士、美国工程院士、美国艺术院士Michael Jordan教授。他和国内大学合作非常密切，2019年聘为厦门大学闽江学者讲座教授。曾在IEEE TPAMI、ICML、NIPS、CVPR、ICCV、ECCV等人工智能领域的顶级期刊和会议上发表论文60余篇，主持美国DARPA、企业研究基金等十多项。主要研究方向是机器学习中的贝叶斯方法。John Paisely is an associate professor in the Department of Electrical Engineering at Columbia University. He is also a member of the Data Science Institute at Columbia. In 2010, he received his Ph.D. in Electrical and Computer Engineering from Duke University. From 2010.01 to 2013.07, he was a post-doc in the Computer Science departments at Princeton University with David Blei and UC Berkeley with Michael Jordan whos is the American Academy of Sciences, American Engineering Fellow, and American Art Academy. He has worked very closely with domestic universities. In 2019, he was appointed as a lecturer at the Xiamen University. He has published more than 60 papers in top journals and conferences in the field of artificial intelligence such as IEEE TPAMI, ICML, NIPS, CVPR, ICCV, ECCV, and presided over more than ten DARPA and corporate research funds in the United States. The main research direction is the Bayesian method in machine learning.

课程简介（中英文）：

本课程将介绍有监督和无监督的机器学习技术，主要涵盖机器学习的概率和非概率方法。本课程的重点内容包括分类和回归模型、聚类方法、矩阵分解和顺序模型等。主要介绍的方法包括线性和逻辑回归、支持向量机、增强、K均值聚类、混合模型、期望最大化算法和隐马尔可夫模型等。同时，包括一些优化算法，如梯度和坐标下降法等。This course provides an introduction to supervised and unsupervised techniques for machine learning. We will cover both probabilistic and non-probabilistic approaches to machine learning. Focus will be on classification and regression models, clustering methods, matrix factorization and sequential models. Methods covered in class include linear and logistic regression, support vector machines, boosting, K-means clustering, mixture models, expectation-maximization algorithm, hidden Markov models, among others.

先修课程：

概率论与数理统计、Matlab