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Lecture Materials

Below Lecture Materials and recommended Book list is provided.

 

Assignments are given in the assignment tab.

 

LECTURE NOTES

BOOKS

Lecture 1 - Sparse Regression and Lasso

 

Hastie, Trevor - Statistical learning with Sparsity : the Lasso and Generalizations, ISBN 9781498712163, CRC Press LLC, 2015

Lecture 2 - Convexity, Lasso Optimization, Lasso Properties, Duality

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Lecture 3 - Penalty, Prox, Loss

Lecture 4 - Statistical Inference

 

Boyd, Stephen P. - Convex Optimization, ISBN 9780521833783, Cambridge University Press, 2004 

 

 

Ben-Tal, Aharon, Nemirovski, Arkadi - Lectures on Modern Convex Optimization: Analysis, Algorithms, and Engineering Applications, ISBN 9780898714913, SIAM, 2001

 

 

Hastie, Trevor - The Elements of Statistical learning : Data Mining, Inference, and Prediction, ISBN 9780387848570, Springer, 2009

 

Lecture 10 - Meta-Learning, Manifold Learning; Representation Learning, Convex Clustering

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Lecture 5 - Matrix and Tensor Decompositions, Approximations and Completion

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Lecture 6 - Graphical Models, Duality, ADMM and prox 

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Lecture 7 - Covariance Selection and Graphical Models

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Lecture 11 - Robustness and Model Mis-Specification Mitigation

 

Jorge Nocedal, Stephen J. Wright - Numerical Optimization, Springer Series in Operations Research and Financial Engineering, ISBN 978-0387303031, Springer, 2006

 

Lecture 9 - Kernels Methods and Multiple Kernel Learning, Deep Learning

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Lecture 8 - Signal Approximation, Compressed Sensing and Sketching, Dictionary Learning

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