cracklsat

以丑续命 万寿无疆

江苏 苏州

感兴趣的主题: Brain 周作人 逻辑 心理学 时间管理 GTD GRE 陈寅恪 LSAT

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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.

Unpredictable? Randomness, Chance and Free Will (Coursera) 0 个评论 关注

开始时间: 01/06/2014 持续时间: 8 weeks

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

简介: This cross-disciplinary course deals with the undetermined, the unpredictable- or what appears to be such. Learn about the usefulness of randomness in communication and computation, the intrinsic randomness of quantum phenomena, the unpredictability of the weather, the role of chance in evolution, and the implications of the neural activity of the brain on our "free will".

StatLearning: Statistical Learning (Stanford Online) 3 个评论 关注

开始时间: 01/20/2014 持续时间: 未知

主页: https://class.stanford.edu/courses/HumanitiesScience/StatLearning/Winter2014/about

简介: This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).

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