不知名的工程师。 修行中..

福建 厦门

感兴趣的主题: 计算广告 机器学习 集成学习 推荐系统


irwenqiang 的课程评论


irwenqiang 关注的课程

Machine Learning (Coursera) 7 个评论 关注

开始时间: 待定 持续时间: Unknown


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!

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

开始时间: 待定 持续时间: 8 weeks


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.

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

开始时间: 02/02/2015 持续时间: 10 weeks


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.

Statistics: Making Sense of Data (Coursera) 1 个评论 关注

开始时间: 04/01/2013 持续时间: 47 weeks


简介: This course is an introduction to the key ideas and principles of the collection, display, and analysis of data to guide you in making valid and appropriate conclusions about the world.

Probabilistic Graphical Models (Coursera) 5 个评论 关注

开始时间: 04/08/2013 持续时间: 11 weeks


简介: In this class, you will learn the basics of the PGM representation and how to construct them, using both human knowledge and machine learning techniques.

Learning From Data (edX) 1 个评论 关注

开始时间: 09/25/2014 持续时间: 10 weeks


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

CVX101: Convex Optimization (Stanford Online) 1 个评论 关注

开始时间: 01/20/2014 持续时间: 未知


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.