15.097 统计与机器学习前沿研讨

MIT Course 15 · Operations Research/Statistics · 课号 15.097

TL;DR

• 统计学习理论与现代高维数据分析交叉前沿，探究过拟合、泛化误差界与正则化。
• Lasso、岭回归与弹性网（Elastic net）揭示 L1/L2 惩罚项在特征选择与稀疏恢复中的几何意义。
• 核方法、支持向量机（SVM）与集成学习算法为复杂非线性高维流形分类提供坚实统计保证。

课程简介 / Description

Prediction is at the heart of almost every scientific discipline, and the study of generalization (that is, prediction) from data is the central topic of machine learning and statistics, and more generally, data mining. Machine learning and statistical methods are used throughout the scientific world for their use in handling the "information overload" that characterizes our current digital age. Machine learning developed from the artificial intelligence community, mainly within the last 30 years, at the same time that statistics has made major advances due to the availability of modern computing. However, parts of these two fields aim at the same goal, that is, of prediction from data. This course provides a selection of the most important topics from both of these subjects.

讲义 / Lecture Notes

• 1
  • Rule mining and the Apriori algorithm (PDF)
• 2
  • R for machine learning (PDF)
  • (Courtesy of Allison Chang. Used with permission.)
• 3
  • Fundamentals of learning (PDF)
• 4
  • Inference (PDF)
• 5
  • Clustering (PDF)
• 6
  • k-nearest neighbors (PDF)
• 7
  • Naïve Bayes (PDF)
• 8
  • Decision trees (PDF)
• 9
  • Logistic regression (PDF)
• 10
  • Boosting (PDF)
• 11
  • Convex optimization (PDF)
• 12
  • Support vector machines (PDF)
• 13
  • Kernels (PDF)
• 14
  • Statistical learning theory (PDF)
• 15
  • Bayesian analysis (PDF - 1.2MB)
  • (Courtesy of Ben Letham. Used with permission.)

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