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《机器学习与数据挖掘》中文版

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课程目的

将理论与实践相结合,获取数据改善其表现,并运用成果。


课程详情

这是涵盖机器学习基本理论、算法和应用的基础课程,结合理论和实践,生动介绍机器学习如何使运算系统得以根据获取的数据改善其表现,并将这些成果运用到工程、科技、金融商业等活动中。专业理论将包括线性模型、VC维、神经网络、支持向量机、数据探测法等。






第1章 学习问题
第2章 学习的可行性
第3章 线性模型
第4章 误差和噪声

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教师

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Yaser Abu-Mostafa
Yaser S. Abu-Mostafa is a Professor of Electrical Engineering and Computer Science at the California Institute of Technology. His main fields of expertise are machine learning 、Pattern Recognition and Data Mining、Information and Complexity、Foundations of Probability and Statistics and computational finance.Dr. Abu-Mostafa received the Clauser Prize for the most original doctoral thesis at Caltech. He received the ASCIT Teaching Awards in 1986, 1989 and 1991, the GSC Teaching Awards in 1995 and 2002, and the Richard P. Feynman prize for excellence in teaching in 1996. He was the founding Program Chairman of the annual conference on Neural Information Processing Systems (NIPS), and a founding member of the IEEE Neural Networks Council. He chaired the second and fourth international conferences on Neural Networks in the Capital Markets (NNCM-94 and NNCM-96), and the sixth international conference on Computational Finance (CF-99). He received the Kuwait State Award in Applied Science in 1999. In 2005, the Hertz Foundation established a perpetual graduate fellowship named the Abu-Mostafa Fellowship in his honor.

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