MIT机器学习导论[麻省理工学院·机器学习视频教程·中文字幕] [推广有奖]

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发表于 2017-10-27 11:05:23 |只看作者 |坛友微信交流群|倒序 |AI写论文

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Introduction to Machine Learning

MIT机器学习导论

MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
麻省理工学院6.0002 2016年秋季计算思维与数据科学导论

About Eric Grimson
Eric Grimson is a Professor of Computer Science and Engineering at the Massachusetts Institute of Technology, and holds the Bernard Gordon Chair of Medical Engineering at MIT. He also holds a joint appointment as a Lecturer on Radiology at Harvard Medical School and at Brigham and Women's Hospital. Between 2011 and 2014 he served as the Chancellor for MIT, having previously served as the Head of the Department of Electrical Engineering and Computer Science at MIT. He currently serves as Chancellor for Academic Advancement for MIT. He received a B.Sc. (High Honors) in Mathematics and Physics from the University of Regina in 1975 and a Ph.D. in Mathematics from MIT in 1980. Prof. Grimson's research group pioneered state of the art systems for activity and behavior recognition, object and person recognition, image database indexing, image guided surgery, site modeling and many other areas of computer vision. Prof. Grimson is a Fellow of the American Association for Artificial Intelligence (AAAI), a Fellow of the IEEE, a Fellow of the ACM, and was awarded the Bose Award for Excellence in Teaching in the School of Engineering at MIT.
Eric Grimson.jpg

Eric Grimson是麻省理工学院计算机科学与工程学教授。他还在哈佛医学院和布里格姆妇女医院担任放射学讲师。2011年至2014年间，他担任麻省理工学院的校长，此前曾担任麻省理工学院电子工程和计算机科学系主任。1975年，他在加拿大里贾纳大学获得了理学学士学位，并在1980年获得了麻省理工学院的数学博士学位。Grimson教授的研究团队开创了活动和行为识别、对象与人识别、图像数据库索引、图像引导手术、网站建模以及计算机视觉等许多领域的先进体系。Grimson教授是美国人工智能协会(AAAI)的成、会员，以及IEEE和ACM的会员，并获得了麻省理工学院工程学院颁发的工程学院卓越教学“Bose奖”。

AWARDS

Association for Computing Machinery: Fellow (2014)
Neural Information Processing Systems (NIPS): Best paper (2010)
Institute of Electrical and Electronics Engineers: Fellow (2004)
Association for the Advancement of Artificial Intelligence : Fellow (2000)

In this lecture, Prof. Grimson introduces machine learning and shows examples of supervised learning using feature vectors.
在本节课中，Grimson教授介绍了机器学习，并展示了使用特征向量进行监督学习的例子。

Introduction to Machine Learning (1)
MIT机器学习导论(一)

Introduction to Machine Learning (2)
MIT机器学习导论(二)

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