萌萌_爱画画

青春不知道怎么就没了。。。

天津 河西区

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Computational Investing, Part I (Coursera) 0 个评论 关注

开始时间: 待定 持续时间: Unknown

主页: https://www.coursera.org/course/compinvesting1

简介: Find out how modern electronic markets work, why stock prices change in the ways they do, and how computation can help our understanding of them.  Build algorithms and visualizations to inform investing practice.

Algorithms, Part I (Coursera) 6 个评论 关注

开始时间: 01/22/2016 持续时间: 6 weeks

主页: https://www.coursera.org/course/algs4partI

简介: This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers basic iterable data types, sorting, and searching algorithms.

Statistics One (Coursera) 4 个评论 关注

开始时间: 待定 持续时间: Unknown

主页: https://www.coursera.org/course/stats1

简介: Statistics One is a comprehensive yet friendly introduction to statistics.

Model Thinking (Coursera) 3 个评论 关注

开始时间: 10/05/2015 持续时间: 10 weeks

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

简介: In this class, you will learn how to think with models and use them to make sense of the complex world around us.

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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