全福亮_

浙江 杭州

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機率 (Coursera) 3 个评论 关注

开始时间: 08/31/2013 持续时间: 10 weeks

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

简介: 這是一個機率的入門課程,著重的是教授機率基本概念。另外我們的作業將搭配台大電機系所開發的多人競技線上遊戲方式,讓同學在遊戲中快樂的學習,快速培養同學們對於機率的洞察力與應用能力。

Introduction to Computational Finance and Financial Econometrics (Coursera) 2 个评论 关注

开始时间: 06/01/2015 持续时间: 10 weeks

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

简介: Learn mathematical and statistical tools and techniques used in quantitative and computational finance. Use the open source R statistical programming language to analyze financial data, estimate statistical models, and construct optimized portfolios. Analyze real world data and solve real world problems.

Introduction to Finance (Coursera) 3 个评论 关注

开始时间: 06/01/2015 持续时间: 15 weeks

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

简介: This course will introduce you to frameworks and tools to measure value; both for corporate and personal assets. It will also help you in decision-making, again at both the corporate and personal levels.

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.

Linear and Integer Programming (Coursera) 3 个评论 关注

开始时间: 待定 持续时间: Unknown

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

简介: This course will cover the very basic ideas in optimization. Topics include the basic theory and algorithms behind linear and integer linear programming along with some of the important applications. We will also explore the theory of convex polyhedra using linear programming.

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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