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所有的机器学习都应该是贝叶斯的吗？所有的贝叶斯模型都应该是非参数的吗？Should all Machine Learning be Bayesian? Should all Bayesian models be non-parametric? |
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| 课程网址: | http://videolectures.net/bark08_ghahramani_samlbb/ |
| 主讲教师: | Zoubin Ghahramani |
| 开课单位: | 剑桥大学 |
| 开课时间: | 2008-10-09 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | I'll present some thoughts and research directions in Bayesian machine learning. I'll contrast black-box approaches to machine learning with model-based Bayesian statistics. Can we meaningfully create Bayesian black-boxes? If so what should the prior be? Is non-parametrics the only way to go? Since we often can't control the effect of using approximate inference, are coherence arguments meaningless? How can we convert the pagan majority of ML researchers to Bayesianism? If the audience gets bored of these philosophical musings, I will switch to talking about our latest technical work on Indian buffet processes. |
| 关 键 词: | 机器学习; 贝叶斯; 模型; 参数 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-10-31：lxf |
| 阅读次数: | 17 |