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Academic Year |
2022/2023 |
Name |
Dott. - MI (1385) Modelli e Metodi Matematici per l'Ingegneria / Mathematical Models and Methods in Engineering |
Programme Year |
1 |
ID Code |
058784 |
Course Title |
STATISTICAL INFERENCE FOR THE INFORMATION AGE |
Course Type |
MONO-DISCIPLINARY COURSE |
Credits (CFU / ECTS) |
5.0 |
Course Description |
Starting from the Bayesian, frequentist and Fisherian classical parametric inference, the course will quickly move to the inferential methodologies introduced in the last decades of the last century and enabled by computers, like empirical Bayes, James Stein estimation and ridge regression, generalized linear models and survival analysis, the bootstrap, cross-validation for estimating prediction error. Finally the course will look at twenty first century topics, like large scale hypothesis testing and false discovery rate, sparse modeling and the lasso, random forests and boosting, inference after model selection, with a final glimpse on nonparametric techniques for estimation, inference and regression.
Students will be required to work, individually or in team, on a real world data analysis project which should explore and put to work some of the methods and algorithms illustrated during the course, or some different and novel methods of data science not part of the course contents but deemed by the class and the instructor to be an eligible and stimulating topic for a course in statistical inference in the computer age. |
Scientific-Disciplinary Sector (SSD)
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Alphabetical group
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Name
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Teaching Assignment Details
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From (included)
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To (excluded)
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A
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ZZZZ
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Secchi Piercesare, Ieva Francesca, Vantini Simone
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