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Statistical Methods with Sparsity (IMEN 891F)
Overview
Course Objectives
This course covers some recent topics in sparse statistical methods.
Textbook
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
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