许亮_在路上

每一天都是很贵的白纸,要在上面写满精彩。(淘宝花名:剑霸)本微博言论仅代表个人观点。

浙江 杭州

感兴趣的主题: 伪文艺青年 数据挖掘 机器学习 户外运动 水瓶座 互联网产品 淘宝 思考ing

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Introduction to Statistics (Udacity) 3 个评论 关注

开始时间: 随时 持续时间: 自主

主页: https://www.udacity.com/course/st101

简介: Statistics is about extracting meaning from data. In this class, we will introduce techniques for visualizing relationships in data and systematic techniques for understanding the relationships using mathematics.

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).

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。]

Machine Learning (Coursera) 28 个评论 关注

开始时间: 待定 持续时间: Unknown

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

简介: Learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.

CVX101: Convex Optimization (Stanford Online) 1 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/Engineering/CVX101/Winter2014/about

简介: This course concentrates on recognizing and solving convex optimization problems that arise in applications. The syllabus includes: convex sets, functions, and optimization problems; basics of convex analysis; least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems; optimality conditions, duality theory, theorems of alternative, and applications; interior-point methods; applications to signal processing, statistics and machine learning, control and mechanical engineering, digital and analog circuit design, and finance.

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