北京 海淀区

感兴趣的主题: 数学 数据挖掘 优化问题 机器学习


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Introduction to Mathematical Thinking (Coursera) 2 个评论 关注

开始时间: 09/21/2015 持续时间: 10 weeks

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

简介: Learn how to think the way mathematicians do - a powerful cognitive process developed over thousands of years.

Learning From Data (edX) 1 个评论 关注

开始时间: 09/25/2014 持续时间: 10 weeks

主页: https://www.edx.org/course/learning-data-caltechx-cs1156x

简介: Introductory Machine Learning course covering theory, algorithms and applications. Our focus is on real understanding, not just "knowing."

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.

Analysis of a Complex Kind (Coursera) 2 个评论 关注

开始时间: 待定 持续时间: Unknown

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

简介: In this course we’ll explore complex analysis, complex dynamics, and some applications of these topics.

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。]

数学之旅 The Journey of Mathematics (Coursera) 0 个评论 关注

开始时间: 12/01/2013 持续时间: 6 weeks

主页: https://www.coursera.org/course/sjtuma153

简介: 课程从问题开始揭示一些数学思想形成的过程,和听众一起从思想上重走一遍前辈们走过的路,体会数学抽象的魅力。 In this course I share the processes which formed the core concepts of mathematical philosophy, walking with students as they experience, learn and enjoy mathematical abstraction.

Machine Learning (Coursera) 7 个评论 关注

开始时间: 待定 持续时间: Unknown

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

简介: Why write programs when the computer can instead learn them from data? In this class you will learn how to make this happen, from the simplest machine learning algorithms to quite sophisticated ones. Enjoy!

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.

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).