xait9363

陕西 西安

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Introduction to Recommender Systems (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/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!

Statistical Analysis of fMRI Data (Coursera) 1 个评论 关注

开始时间: 02/09/2015 持续时间: 6 weeks

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

简介: Explore the intersection of statistics and functional magnetic resonance imaging (fMRI), a non-invasive technique for studying brain activity.

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).

An Introduction to Interactive Programming in Python (Coursera) 5 个评论 关注

开始时间: 待定 持续时间: Unknown

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

简介: This course is designed to be a fun introduction to the basics of programming in Python. Our main focus will be on building simple interactive games such as Pong, Blackjack and Asteroids.

機器學習技法 (Machine Learning Techniques) (Coursera) 2 个评论 关注

开始时间: 11/10/2015 持续时间: 8 weeks

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

简介: The course extends the fundamental tools in "Machine Learning Foundations" to powerful and practical models by three directions, which includes embedding numerous features, combining predictive features, and distilling hidden features. [這門課將先前「機器學習基石」課程中所學的基礎工具往三個方向延伸為強大而實用的工具。這三個方向包括嵌入大量的特徵、融合預測性的特徵、與萃取潛藏的特徵。]

Process Mining: Data science in Action (Coursera) 0 个评论 关注

开始时间: 10/07/2015 持续时间: 8 weeks

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

简介: Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains.

Data Analysis and Statistical Inference (Coursera) 1 个评论 关注

开始时间: 待定 持续时间: Unknown

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

简介: This course introduces you to the discipline of statistics as a science of understanding and analyzing data. You will learn how to effectively make use of data in the face of uncertainty: how to collect data, how to analyze data, and how to use data to make inferences and conclusions about real world phenomena.

Pattern Discovery in Data Mining (Coursera) 1 个评论 关注

开始时间: 02/09/2015 持续时间: 4 weeks

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

简介: Learn the basic concepts of data mining and dive deep into pattern discovery methods and their applications.

算法基础 (Coursera) 0 个评论 关注

开始时间: 07/21/2015 持续时间: 8 weeks

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

简介: 本课程内容程涵盖枚举、二分、贪心、递归、深度优先搜索、广度优先搜索、动态规划等基本算法。通过大量的高强度的编程训练,提高动手能力,做到能较为熟练、完整、准确地实现自己设计的程序,为进一步学习其他计算机专业课程,或在其他专业领域运用计算机编程解决问题奠定良好的基础。

Java程序设计 (Coursera) 1 个评论 关注

开始时间: 04/13/2015 持续时间: 12 weeks

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

简介: 《Java程序设计》课程是使用Java语言进行应用程序设计的课程,针对各专业的大学本科生开设。课程的主要目标有三: 一、掌握Java语言的语法,能够较为深入理解Java语言机制,掌握Java语言面向对象的特点。 二、掌握JavaSE中基本的API,掌握在集合、线程、输入输出、图形用户界面、网络等方面的应用。三、能够编写有一定规模的应用程序,养成良好的编程习惯,会使用重构、设计模式、单元测试、日志、质量管理工具提高代码的质量。 对于学过“计算机基础、计算概论或C语言的学生”尤为适用。

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