南京大学《高级机器学习》.rar

上传者: 43909715 | 上传时间: 2021-08-24 20:01:29 | 文件大小: 147.39MB | 文件类型: RAR
南京大学李宇峰老师在2021年开设的高级学习课程,以周志华老师西瓜书为教材,讲述了从模型选择、到强化学习等高级机器学习主题,可供学习参考。 目录内容: Lecture 1: Basic info for the course [Slide] Introduction [Slide] Lecture 2: Model selection [Slide] Lecture 3: Linear Model [Slide] Support Vector Machine [Slide] Neural Network [Slide] Decision Tree [Slide] Bayesian Model [Slide] Lecture 4: Ensemble Learning [Slide] Lecture 5: Clustering [Slide] Lecture 6: Dimension Reduction [Slide] Lecture 7: Feature Selection [Slide] Lecture 8: Semi-Supervised Learning [Slide] Lecture 9: Multi-Label Learning* [Slide] Lecture 10: Graphical Model [Slide] Lecture 11: Reinforcement Learning [Slide] 《机器学习》(西瓜书) 1-10章和11章之后的区别 • 1-10章,主要介绍了机器学习基本原理和经典 模型(机器学习是有理论基础,有直觉原理的) • 11章后,开始涉及复杂学习模型,应对复杂现 实数据问题(现实世界问题是复杂的)

文件下载

资源详情

[{"title":"( 15 个子文件 147.39MB ) 南京大学《高级机器学习》.rar","children":[{"title":"南京大学《高级机器学习》","children":[{"title":"Lecture5 Clustering.pdf <span style='color:#111;'> 5.74MB </span>","children":null,"spread":false},{"title":"Lecture6 Dimension Reduction.pdf <span style='color:#111;'> 8.76MB </span>","children":null,"spread":false},{"title":"Lecture7 Feature Selection.pdf <span style='color:#111;'> 2.19MB </span>","children":null,"spread":false},{"title":"Lecture3 Linear Model、Support Vector Machine、Neural Network、Decision Tree、Bayesian Model","children":[{"title":"Lecture03A.pdf <span style='color:#111;'> 9.03MB </span>","children":null,"spread":false},{"title":"Lecture03D.pdf <span style='color:#111;'> 4.53MB </span>","children":null,"spread":false},{"title":"Lecture03E.pdf <span style='color:#111;'> 5.31MB </span>","children":null,"spread":false},{"title":"Lecture03B.pdf <span style='color:#111;'> 4.18MB </span>","children":null,"spread":false},{"title":"Lecture03C.pdf <span style='color:#111;'> 19.37MB </span>","children":null,"spread":false}],"spread":true},{"title":"Lecture1 Introduction.pdf <span style='color:#111;'> 48.04MB </span>","children":null,"spread":false},{"title":"Lecture10 Graphical Model.pdf <span style='color:#111;'> 12.95MB </span>","children":null,"spread":false},{"title":"Lecture2 Model selection.pdf <span style='color:#111;'> 6.19MB </span>","children":null,"spread":false},{"title":"Lecture8 Semi-Supervised Learning.pdf <span style='color:#111;'> 23.82MB </span>","children":null,"spread":false},{"title":"Lecture11 Reinforcement Learning.pdf <span style='color:#111;'> 1.50MB </span>","children":null,"spread":false},{"title":"Lecture4 Ensemble Learning.pdf <span style='color:#111;'> 16.57MB </span>","children":null,"spread":false},{"title":"Lecture9 Multi-Label Learning.pdf <span style='color:#111;'> 40.71MB </span>","children":null,"spread":false}],"spread":false}],"spread":true}]

评论信息

免责申明

【只为小站】的资源来自网友分享,仅供学习研究,请务必在下载后24小时内给予删除,不得用于其他任何用途,否则后果自负。基于互联网的特殊性,【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查;无论 【只为小站】 经营者是否已进行审查,用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场,基于网友分享,根据中国法律《信息网络传播权保护条例》第二十二条之规定,若资源存在侵权或相关问题请联系本站客服人员,zhiweidada#qq.com,请把#换成@,本站将给予最大的支持与配合,做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明