mihaca11

上海 杨浦区

感兴趣的主题: 教育 校园生活 IT数码 科学探索 上海生活

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db: Introduction to Databases (Stanford Online) 1 个评论 关注

开始时间: 01/06/2014 持续时间: 未知

主页: https://class.stanford.edu/courses/Engineering/db/2014_1/about

简介: "Introduction to Databases" was one of Stanford's inaugural three massive open online courses in the fall of 2011 and was offered again in early 2013. January 2014 will mark its third offering. The course includes video lectures and demos with in-video quizzes to check understanding, in-depth standalone quizzes, a wide variety of automatically-checked interactive programming exercises, midterm and final exams, a discussion forum, optional additional exercises with solutions, and pointers to readings and resources. Taught by Professor Jennifer Widom, the curriculum draws from Stanford's popular Introduction to Databases course.

機器學習基石 (Machine Learning Foundations) (Coursera) 10 个评论 关注

开始时间: 09/08/2015 持续时间: 8 weeks

主页: https://www.coursera.org/course/ntumlone

简介: Machine learning is the study that allows computers to adaptively improve their performance with experience accumulated from the data observed. The course teaches the most fundamental algorithmic, theoretical and practical tools that any user of machine learning needs to know. [機器學習旨在讓電腦能由資料中累積的經驗來自我進步。本課程將介紹各領域中的機器學習使用者都應該知道的基礎演算法、理論及實務工具。]

機器學習技法 (Machine Learning Techniques) (Coursera) 2 个评论 关注

开始时间: 11/10/2015 持续时间: 8 weeks

主页: https://www.coursera.org/course/ntumltwo

简介: The course extends the fundamental tools in "Machine Learning Foundations" to powerful and practical models by three directions, which includes embedding numerous features, combining predictive features, and distilling hidden features. [這門課將先前「機器學習基石」課程中所學的基礎工具往三個方向延伸為強大而實用的工具。這三個方向包括嵌入大量的特徵、融合預測性的特徵、與萃取潛藏的特徵。]

Mining Massive Datasets (Coursera) 1 个评论 关注

开始时间: 09/12/2015 持续时间: 7 weeks

主页: https://www.coursera.org/course/mmds

简介: This class teaches algorithms for extracting models and other information from very large amounts of data. The emphasis is on techniques that are efficient and that scale well.

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