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分享两个斯坦福线上课程：人工智能AI和机器学习ML
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AI:http://www.douban.com/online/10918517/
https://blog.sciencenet.cn/blog-240604-484187.html
斯坦福于今年十月份将开始网络课程【人工智能】。所有内容线上发布，包括视频讲座，作业，考试等等。如果顺利完成，将得到证书一个。
全球目前已经有超过五十六万学生报名。
课程内容为全英文，但是youtube有自动翻译中文字幕（不是很精准）。
本活动旨在为希望参与这门课的同学一个交流的平台。希望大家多多推荐。
详情见 http://www.ai-class.com/
A bold experiment in distributed education, "Introduction to Artificial Intelligence" will be offered free and online to students worldwide during the fall of 2011. The course will include feedback on progress and a statement of accomplishment. Taught by Sebastian Thrun and Peter Norvig, the curriculum draws from that used in Stanford's introductory Artificial Intelligence course. The instructors will offer similar materials, assignments, and exams.
Artificial Intelligence is the science of making computer software that reasons about the world around it. Humanoid robots, Google Goggles, self-driving cars, even software that suggests music you might like to hear are all examples of AI. In this class, you will learn how to create this software from two of the leaders in the field. Class begins October 10.
Details on the course, including a syllabus is available here. Sign up above to receive additional information about participating in the online version when it becomes available
全球目前已经有超过五十六万学生报名。
课程内容为全英文，但是youtube有自动翻译中文字幕（不是很精准）。
本活动旨在为希望参与这门课的同学一个交流的平台。希望大家多多推荐。
详情见 http://www.ai-class.com/
A bold experiment in distributed education, "Introduction to Artificial Intelligence" will be offered free and online to students worldwide during the fall of 2011. The course will include feedback on progress and a statement of accomplishment. Taught by Sebastian Thrun and Peter Norvig, the curriculum draws from that used in Stanford's introductory Artificial Intelligence course. The instructors will offer similar materials, assignments, and exams.
Artificial Intelligence is the science of making computer software that reasons about the world around it. Humanoid robots, Google Goggles, self-driving cars, even software that suggests music you might like to hear are all examples of AI. In this class, you will learn how to create this software from two of the leaders in the field. Class begins October 10.
Details on the course, including a syllabus is available here. Sign up above to receive additional information about participating in the online version when it becomes available
开始时间: 2011年10月10日 周一 07:00
结束时间: 2011年12月6日 周二 06:00
结束时间: 2011年12月6日 周二 06:00
斯坦福于今年十月份将公开授课的线上课程【机器学习】。所有内容免费线上发布，包括视频讲座，作业，考试等等。如果顺利完成，将得到证书一个。
课程内容为全英文，youtube有自动翻译的中文字幕。
本活动旨在为希望参与这门课的同学一个交流的平台。希望大家多多推荐。
详情见 http://www.ml-class.com/
Course Description
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). (iv) Reinforcement learning. The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
课程内容为全英文，youtube有自动翻译的中文字幕。
本活动旨在为希望参与这门课的同学一个交流的平台。希望大家多多推荐。
详情见 http://www.ml-class.com/
Course Description
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). (iv) Reinforcement learning. The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
https://blog.sciencenet.cn/blog-240604-484187.html
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