陈敏Cmagic

活着就是为了改变世界!

黑龙江 哈尔滨

感兴趣的主题: IT 计算机 经济

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Machine Learning (CourseraArchive) 29 个评论 关注

开始时间: 04/22/2022 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.

Coding the Matrix: Linear Algebra through Computer Science Applications (CourseraArchive) 9 个评论 关注

开始时间: 04/22/2022 持续时间: 10 weeks

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

简介: Learn the concepts and methods of linear algebra, and how to use them to think about computational problems arising in computer science. Coursework includes building on the concepts to write small programs and run them on real data.

Social and Economic Networks: Models and Analysis (CourseraArchive) 3 个评论 关注

开始时间: 04/22/2022 持续时间: 9 weeks

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

简介: Learn how to model social and economic networks and their impact on human behavior. How do networks form, why do they exhibit certain patterns, and how does their structure impact diffusion, learning, and other behaviors? We will bring together models and techniques from economics, sociology, math, physics, statistics and computer science to answer these questions.

Introduction to Recommender Systems (CourseraArchive) 3 个评论 关注

开始时间: 04/22/2022 持续时间: Unknown

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

简介: This course introduces the concepts, applications, algorithms, programming, and design of recommender systems--software systems that recommend products or information, often based on extensive personalization. Learn how web merchants such as Amazon.com personalize product suggestions and how to apply the same techniques in your own systems!

Econometrics (MIT公开课) 0 个评论 关注

开始时间: 04/22/2022 持续时间: 未知

主页: http://ocw.mit.edu/courses/economics/14-32-econometrics-spring-2007

简介: Introduction to econometric models and techniques, simultaneous equations, program evaluation, emphasizing regression. Advanced topics include instrumental variables, panel data methods, measurement error, and limited dependent variable models. May not count toward HASS requirement.

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