吴竑

勋章:喜欢Code、篮球、Indie Games、Dota、女人和胖子。

北京 海淀区

感兴趣的主题: code 理工男 籃球 DOTA

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Probabilistic Graphical Models (Coursera) 5 个评论 关注

开始时间: 04/08/2013 持续时间: 11 weeks

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

简介: In this class, you will learn the basics of the PGM representation and how to construct them, using both human knowledge and machine learning techniques.

Algorithms, Part II (Coursera) 5 个评论 关注

开始时间: 03/16/2016 持续时间: 6 weeks

主页: https://www.coursera.org/course/algs4partII

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.

The Hardware/Software Interface (Coursera) 7 个评论 关注

开始时间: 06/30/2014 持续时间: 8 weeks

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

简介: Examines key computational abstraction levels below modern high-level languages. From Java/C to assembly programming, to basic processor and system organization.

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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