Statistical methods for data analysis (2018/2019)



Course code
4S007624
Credits
6
Coordinator
Alberto Castellini
Academic sector
MAT/08 - NUMERICAL ANALYSIS
Language of instruction
English
Teaching is organised as follows:
Activity Credits Period Academic staff Timetable
Machine learning 3 I semestre Alberto Castellini

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Statistical modelling 3 I semestre Leonard Peter Bos

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Learning outcomes

The objective is to introduce students to statistical modelling and exploratory
data analysis. The mathematical foundations of Statistical Learning (supervised
and unsupervised learning, deep learning) are developed with emphasis on the
underlying abstract mathematical framework, aiming to provide a rigorous,
self-contained derivation and theoretical analysis of the main models currently
used in applications. Complimentary laboratory sessions will illustrate the use
of both the key algorithms and relevant case studies, mainly by using standard
software environments such as R or Python.

Syllabus

- Introduction to data analysis with R and Python

- Linear methods for regression (linear regression, least squares, MLE: Estimation, Prediction, Tests under Gaussian assumptions, variable/subset selection

- Shrinkage/Regularization methods (Ridge regression, Least absolute shrinkage and selection operator, [Elastic net, Least angle regression])

- Linear methods for classification (Logistic regression, MLE: estimation, prediction, variable selection)

- Linear model assessment and selection (cross-validation, bootstrap methods)

- Clustering analysis (k-means, principal component analysis and spectral clustering)

Assessment methods and criteria

The purpose of the exam is to evaluate the capabilities of the student to understand and use the methodologies presented in the course. The exam consists of a project assignment about specific case studies. Alternatively, the student may choose to give a public presentation about advanced methodologies from the literature related to the topics of the course.

Reference books
Activity Author Title Publisher Year ISBN Note
Machine learning T. Hastie, R. Tibshirani, J. Friedman. The elements of statistical learning. Data mining, inference, and prediction. (Edizione 2) Springer 2009

STUDENT MODULE EVALUATION - 2017/2018