Machine Learning

开始时间: 待定 持续时间: Unknown

所在平台: Coursera

课程类别: 计算机科学

大学或机构: University of Washington(华盛顿大学)

授课老师: Pedro Domingos



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课程评论: 7 个评论

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Machine learning algorithms can figure out how to perform important tasks by generalizing from examples. This is often feasible and cost-effective when manual programming is not. Machine learning (also known as data mining, pattern recognition and predictive analytics) is used widely in business, industry, science and government, and  there is a great shortage of experts in it. If you pick up a machine learning textbook you may find it forbiddingly mathematical, but in this class you will learn that the key ideas and algorithms are in fact quite intuitive. And powerful!

Most of the class will be devoted to supervised learning (in other words, learning in which a teacher provides the learner with the correct answers at training time). This is the most mature and widely used type of machine learning. We will cover the main supervised learning techniques, including decision trees, rules, instances, Bayesian techniques, neural networks, model ensembles, and support vector machines. We will also touch on learning theory with an emphasis on its practical uses. Finally, we will cover the two main classes of unsupervised learning methods: clustering and dimensionality reduction. Throughout the class there will be an emphasis not just on individual algorithms but on ideas that cut across them and tips for making them work.

In the class projects you will build your own implementations of machine learning algorithms and apply them to problems like spam filtering, clickstream mining, recommender systems, and computational biology. This will get you as close to becoming a machine learning expert as you can in ten weeks!


Week One: Basic concepts in machine learning.
Week Two: Decision tree induction.
Week Three: Learning sets of rules and logic programs.
Week Four: Instance-based learning.
Week Five: Statistical learning.
Week Six: Neural networks.
Week Seven: Model ensembles.
Week Eight: Learning theory.
Week Nine: Support vector machines.
Week Ten: Clustering and dimensionality reduction.



陈炳炎ds 2016-09-01 14:30 0 票支持; 0 票反对



树下的龙猫 2014-10-23 16:15 1 票支持; 1 票反对



张鹏2008 2014-01-03 09:30 0 票支持; 0 票反对



CHANTZ_ 2013-12-26 19:48 0 票支持; 0 票反对



不像Andrew ng的课,andrew的课适合入门,但是比较零散

University of Washington的这门课,刚开始的概要性介绍很好,机器学习的一般性框架,一般性处理,那么多种算法在整个框架中处在什么位置非常一目了然



小马哥07DM 2013-05-22 13:31 1 票支持; 1 票反对

跟Ng讲的东西很不一样,内容大部分来源于Tom Mitchell的书。


52nlp 2013-05-22 13:03 3 票支持; 1 票反对

这门课的老师是机器学习的大牛Pedro Domingos,他写过的“"A Few Useful Things to Know about Machine Learning”在微博广为流传,这门课虽然没有正式开始,但是通过preview的链接可以看课程的所有视频


wzyer 2013-05-17 09:22 2 票支持; 1 票反对


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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!


数据挖掘 机器学习 机器学习入门 模式识别 华盛顿大学 UW



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