Computational Statistics

We discuss modern statistical methods for data analysis, including methods for data exploration, prediction and inference. We pay attention to algorithmic aspects, theoretical properties and practical considerations. The class is hands-on and methods are applied using the statistical programming language R.

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An introduction to statistical learning

With applications in R

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

Signature Year Rack/Shelf Lecture Return Date Status
Textbook.M.066.03.2 2017 9 / 2 Computational Statistics Available
Textbook.M.066.03.3 2017 9 / 2 Computational Statistics Available
Textbook.M.066.03.1 2017 9 / 2 Computational Statistics 16.12.2024 Unavailable
AVAILABLE
READING ROOM ONLY
NOT AVAILABLE
ONLINE VERSION
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The elements of statistical learning

Data mining, inference, and prediction

Trevor Hastie, Robert Tibshirani, Jerome Friedman

Signature Year Rack/Shelf Lecture Return Date Status
Textbook.M.066.04.2 2017 9 / 2 Computational Statistics Available
Textbook.M.005.11.1 2016 7 / 4 Advanced Machine Learning Available
Textbook.M.005.11.2 2016 7 / 4 Advanced Machine Learning Available
Textbook.M.005.06.1 2009 7 / 4 Advanced Machine Learning Available
Textbook.M.030.05.1 2009 12 / 3 Statistical Learning Theory Unavailable
Textbook.M.066.04.1 2017 9 / 2 Computational Statistics 17.12.2024 Unavailable
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READING ROOM ONLY
NOT AVAILABLE
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Modern applied statistics with S

W.N. Venables, B.D. Ripley

Signature Year Rack/Shelf Lecture Return Date Status
Textbook.M.066.02.1 2010 9 / 2 Computational Statistics Available
Textbook.M.066.02.2 2010 9 / 2 Computational Statistics On-site use only
AVAILABLE
READING ROOM ONLY
NOT AVAILABLE
ONLINE VERSION
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Elements of computational statistics

James E. Gentle

Signature Year Rack/Shelf Lecture Return Date Status
Textbook.M.066.01.1 2005 9 / 2 Computational Statistics Available
Textbook.M.066.01.2 2005 9 / 2 Computational Statistics On-site use only