COS424: Interacting with Data

Princeton University
Spring, 2012

Course Materials

• Introduction
  
  
• Probability and Statistics Review
  • Card Trick R demo
  
  
• Homework 1 (due February 28, 2012)
  
  
• Model-Based Classification  
  • Gaussian classification R demo
  • Data
  
  
• Homework 2 (due March 15, 2012)
  • Zipfile of files you need
  
  
• K-means
  
  
• Mixture models
  
  
• Expectation-maximization
  • Gaussian mixture R demo
  
  
• Hidden Markov models
  • Written supplement
  
  
• Homework 3 (due April 12, 2012)
  
  
• Linear regression and regularized linear regression
  • Lecture notes
  • Slides
  • R demo
  
  
• Generalized linear models
  
  
• Dimension reduction
  • Lecture notes
  • Collaborative filtering R demo
  
  
• Scalable data analysis
  
  
• Probabilistic topic modeling
  
  
• Final lecture of the course

Announcements

• [2012-04-24] Lee and Seung, 1999
  
  
• [2012-03-14] Notes on evaluating models and the final project.
  
  
• [2012-02-13] Here are some resources for learning and using R:
  • Download R at the R Project for Statistical Computing.
  • Start to learn R by reading Introductory Statistics with R by Peter Dalgaard (Ch 1-2).
  • Many people like R studio.
  • Dave uses Emacs Speaks Statistics.
  
  
• [2012-02-08] The Hastie et al. book can be found here.