is_dhy

湖北 武汉

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Algorithms, Part I (Coursera) 6 个评论 关注

开始时间: 01/22/2016 持续时间: 6 weeks

主页: https://www.coursera.org/course/algs4partI

简介: This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers basic iterable data types, sorting, and searching algorithms.

Statistics One (Coursera) 4 个评论 关注

开始时间: 待定 持续时间: Unknown

主页: https://www.coursera.org/course/stats1

简介: Statistics One is a comprehensive yet friendly introduction to statistics.

Analysis of Algorithms (Coursera) 1 个评论 关注

开始时间: 03/04/2016 持续时间: 6 weeks

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

简介: This course teaches a calculus that enables precise quantitative predictions of large combinatorial structures. In addition, this course covers generating functions and real asymptotics and then introduces the symbolic method in the context of applications in the analysis of algorithms and basic structures such as permutations, trees, strings, words, and mappings.

db: Introduction to Databases (Stanford Online) 1 个评论 关注

开始时间: 01/06/2014 持续时间: 未知

主页: https://class.stanford.edu/courses/Engineering/db/2014_1/about

简介: "Introduction to Databases" was one of Stanford's inaugural three massive open online courses in the fall of 2011 and was offered again in early 2013. January 2014 will mark its third offering. The course includes video lectures and demos with in-video quizzes to check understanding, in-depth standalone quizzes, a wide variety of automatically-checked interactive programming exercises, midterm and final exams, a discussion forum, optional additional exercises with solutions, and pointers to readings and resources. Taught by Professor Jennifer Widom, the curriculum draws from Stanford's popular Introduction to Databases course.

Computing for Data Analysis (Coursera) 7 个评论 关注

开始时间: 09/23/2013 持续时间: 4 weeks

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

简介: This course is about learning the fundamental computing skills necessary for effective data analysis. You will learn to program in R and to use R for reading data, writing functions, making informative graphs, and applying modern statistical methods.

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).

Social Network Analysis (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/sna

简介: This course will use social network analysis, both its theory and computational tools, to make sense of the social and information networks that have been fueled and rendered accessible by the internet.

Gamification (Coursera) 4 个评论 关注

开始时间: 待定 持续时间: Unknown

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

简介: Gamification is the application of game elements and digital game design techniques to non-game problems, such as business and social impact challenges. This course will teach you the mechanisms of gamification, why it has such tremendous potential, and how to use it effectively.

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.

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