Statistical Methods with Sparsity (IMEN 891F)


Course Objectives

This course covers some recent topics in sparse statistical methods.


  • Hastie, T., Tibshirani, R., and Wainwright, M. (2015). Statistical Learning with Sparsity: The Lasso and Generalizations. CRC Press.

Lecture Slides

  • Supplement: Convex Optimization and Karush-Kuhn-Tucker Condition [Slides]

  • Chapter 1: Introduction [Slides]

  • Chapter 2: The Lasso for Linear Models [Slides]

  • Chapter 3: Generalized Linear Models [Slides]

  • Chapter 4: Generalizations of the Lasso Penalty [Slides]

  • Chapter 5: Optimization Methods [Slides]

  • Chapter 6: Statistical Inference [Slides]

  • Chapter 7: Matrix Decompositions, Approximations and Completion [Slides]

  • Chapter 8: Sparse Multivariate Methods [Slides]

  • Chapter 9: Graphs and Model Selection [Slides]

  • Chapter 10: Signal Approximation and Compressed Sensing [Slides]

  • Chapter 11: Theoretical Results for the Lasso [Slides]

Lecture Videos