mal-016.dvi
Foundations and Trends R � in Machine Learning Vol. 3, No. 1 (2010) 1–122 c � 2011 S. Boyd, N. Parikh, E. Chu, B. Peleato and J. Eckstein DOI: 10.1561/2200000016 Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers Stephen Boyd1 , Neal Parikh2 , Eric Chu3 Borja Peleato4 and Jonathan Eckstein5 1 Electrical Engineering Department, Stanford University,
Computer Age Statistical Inference: Algorithms, Evidence and Data Science
The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. “Big data,” “data science,” and “machine learning” have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on a journey through th
Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares
Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares Stephen Boyd and Lieven Vandenberghe Cambridge University Press This book is used as the textbook for our own courses ENGR108 (Stanford) and EE133A (UCLA), where you will find additional related material. If you find an error not listed in our
Alternating Direction Method of Multipliers
Alternating Direction Method of Multipliers Prof S. Boyd EE364b, Stanford University source: Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers (Boyd, Parikh, Chu, Peleato, Eckstein) 1 Goals robust methods for ◮ arbitrary-scale optimization – machine learning/statistics with huge data-sets – dynamic optimization on large-scale network ◮ decentrali
Convex Optimization – Boyd and Vandenberghe
A MOOC on convex optimization, CVX101, was run from 1/21/14 to 3/14/14. If you register for it, you can access all the course materials. More material can be found at the web sites for EE364A (Stanford) or EE236B (UCLA), and our own web pages. Source code for almost all examples and figures in part 2 of the book is available in CVX (in the examples directory), in CVXOPT (in the book examples direc
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http://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html
Stanford University: Tensorflow for Deep Learning Research
http://web.stanford.edu/group/mmds/slides2008/lim.pdf
Elements of Statistical Learning: data mining, inference, and prediction. 2nd Edition.
http://web.stanford.edu/class/ee380/Abstracts/160106-slides.pdf
http://web.stanford.edu/class/ee486/doc/chap2.pdf
https://web.stanford.edu/class/cs229t/