开课单位--芝加哥大学
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Using Data for Social Good[利用数据造福社会]
   Rayid Ghani(芝加哥大学) This talk will discuss ways in which data, analytics, machine learning, and all other related buzzwords can be used to solve problems with social impa...
热度：41

2
Opening Remarks at the Chandrasekhar Centennial Symposium 2010[2010钱德拉塞卡百年纪念研讨会开场白]
  Robert M. Wald(芝加哥大学) Opening Remarks at the Chandrasekhar Centennial Symposium 2010.
热度：25

3
Joint Learning of Multiple Structured Output Prediction Tasks[多结构输出预测任务的联合学习]
  Yasemin Altun(芝加哥大学)
热度：31

4
Geometric Methods and Manifold Learning[几何方法与流形学习]
  Mikhail Belkin; Partha Niyogi(芝加哥大学)
热度：29

5
Semi-supervised Learning by Higher Order Regularization[高阶正则化的半监督学习]
  Xueyuan Zhou(芝加哥大学) In semi-supervised learning, at the limit of infinite unlabeled points while fixing labeled ones, the solutions of several graph Laplacian regularizat...
热度：27

6
The Dark Side of the Universe: Dark Matter and Dark Energy[宇宙的黑暗面：暗物质和暗能量]
  Michael Turner(芝加哥大学) 宇宙的黑暗面：暗物质和暗能量
热度：46

7
Improved Regret Guarantees for Online Smooth Convex Optimization with Bandit Feedback[改进的后悔保证与强盗反馈在线平滑凸优化]
  Ankan Saha(芝加哥大学) The study of online convex optimization in the bandit setting was initiated by Kleinberg (2004) and Flaxman et al. (2005). Such a setting models a dec...
热度：20

8
How to Visualize the Unseeable[如何可视化看不见的东西 ]
  Xiaochuan Pan(芝加哥大学) 如何可视化看不见的东西
热度：37

9
Research Directions in Enterprise Knowledge Management[企业知识管理研究方向]
  Rayid Ghani(芝加哥大学)
热度：18

10
Concentration-Based Guarantees for Low-Rank Matrix Reconstruction[基于集中的低秩矩阵重构保证]
  Rina Foygel(芝加哥大学) We consider the problem of approximately reconstructing a partially-observed, approximately low-rank matrix. This problem has received much attention ...
热度：95
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