哥伦比亚大学(ColumbiaUniversity),位于美国纽约市曼哈顿,1754年成立,属于私立的常春藤盟校。由三个本科生院和十三个研究生院构成。现有教授三千多人,学生两万余人,校友25万人遍布世界150多个国家。学校每年经费预算约20亿美元,图书馆藏书870万册。

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COMS 4771是一个研究生水平的机器学习入门。本课程涵盖监督机器学习的基本统计原理,以及一些常见的算法范例。

https://www.cs.columbia.edu/~djhsu/coms4771-f20/#description

主题:

  • Overview of machine learning
  • Nearest neighbors
  • Prediction theory
  • Regression I: Linear regression
  • Regression II: Regularization
  • Multivariate Gaussians and PCA
  • Regression III: Kernels
  • Classification I: Linear classification
  • Optimization I: Convex optimization
  • Classification II: Margins and SVMs
  • Classification III: Classification objectives
  • Optimization II: Neural networks
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最新论文

Recent query explanation systems help users understand anomalies in aggregation results by proposing predicates that describe input records that, if deleted, would resolve the anomalies. However, it can be difficult for users to understand how a predicate was chosen, and these approaches are limited to errors that can be resolved through deletion. In contrast, data errors may be due to group-wise errors, such as missing records or systematic value errors. This paper presents Reptile, an explanation system for hierarchical data. Given an anomalous aggregate query result, Reptile recommends the next drill-down attribute,and ranks the drill-down groups based on the extent repairing the group's statistics to its expected values resolves the anomaly. Reptile efficiently trains a multi-level model that leverages the data's hierarchy to estimate the expected values, and uses a factorised representation of the feature matrix to remove redundancies due to the data's hierarchical structure. We further extend model training to support factorised data, and develop a suite of optimizations that leverage the data's hierarchical structure. Reptile reduces end-to-end runtimes by more than 6 times compared to a Matlab-based implementation, correctly identifies 21/30 data errors in John Hopkin's COVID-19 data, and correctly resolves 20/22 complaints in a user study using data and researchers from Columbia University's Financial Instruments Sector Team.

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