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ORF 350 (Big Data): Spring 2017

This version of the course is no longer taught at Princeton as Prof. Han Liu is now at Northwestern.

Course Contents

  • Limiting theorems and different modes of stochastic convergence
  • Maximum likelihood estimation
  • Prediction and variable selection
  • Risk minimization perspective of regression
  • Overfitting and regularization
  • Model selection
  • Bias-variance tradeoff
  • High dimensional inference
  • Ridge, bridge, lasso, elastic-net regression
  • Geometric interpretation of different shrinkage estimators
  • Bayes rule and Bayes risk
  • Generative vs. discriminant classification
  • Geometric interpretation of different loss functions
  • Support vector machines
  • Quadratic discriminant analysis, linear discriminant analysis, diagonal linear discriminant anslysis
  • Naive Bayes classification
  • Unsupervised learning
  • Clustering and finite mixture models
  • EM algorithm
  • Continuous latent variable models
  • Dimensionality reduction

Assignments

  1. Central Limit Theory, Law of Large Numbers and R Programming
  2. Maximum Likelihood and Regression
  3. High Dimensional Regression
  4. Logistic Regression
  5. Clustering Analysis and EM Algorithm
  6. Probabilistic Graphical Model

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Assignments from ORF 350 (Big Data) Spring 2017 taught by Han Liu

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