高珩_monnnnnnnnsterkill

真诚是我永恒的标签

北京 海淀区

感兴趣的主题: 篮球 电影 机器学习 数据挖掘 社交网络 音乐 羽毛球 处女座 信息处理 游泳

1个粉丝

高珩_monnnnnnnnsterkill 的课程评论

更多评论

高珩_monnnnnnnnsterkill 关注的课程

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.

Neural Networks for Machine Learning (Coursera) 5 个评论 关注

开始时间: 待定 持续时间: 8 weeks

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

简介: Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well.

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!

Algorithms, Part II (Coursera) 5 个评论 关注

开始时间: 03/16/2016 持续时间: 6 weeks

主页: https://www.coursera.org/course/algs4partII

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.

Computational Methods for Data Analysis (Coursera) 1 个评论 关注

开始时间: 01/05/2015 持续时间: 10 weeks

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

简介:

Exploratory and objective data analysis methods applied to the physical, engineering, and biological sciences.

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).

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.

更多课程