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Single discipline educational activity
Course Sheet Academic Year of enrolment:
Professor and Collaborators:
Hours of classroom activity:
Prerequisites:
Mathematics (calculus), linear algebra and statistical inference (estimation and statistical test)
Objectives
Contents The following topics are considered as important parts of the teaching program for the fulfilment of the objectives: introduction to Data Mining
and Statistical Learning, data visualization, regression and classification, Non-supervised learning (principal component analysis and clustering)
Extended Syllabus The course aims to introduce methods and models to extract relevant information from large amounts of data, with particular attention to
statistical learning (statistical learning) both in a predictive and nonpredictive context (supervised and non-supervised learning). In order to
provide the skills for the analysis and modeling of real data, the lessons will be supplemented by R exercises in the computer room.
Program:
Introduction to data mining and statistical learning.
Data visualization techniques
Regression and Classification: multiple linear regression, discriminant analysis and K-nearest
neighbors.
Non-linear methods (flexible regression): polynomial regression and generalized additive models.
Unsupervised learning: association rules, principal component analysis, grouping methods (hierarchical Clustering and mixtures).
Recommended Bibliography Slides and handouts for students not attending the course will be available from the professor
James, Witten, Hastie, Tibshirani (2013) An Introduction to Statistical Learning (with Applications in R), Springer-Verlag
Hastie, Tibshirani, Friedman (2009) The elements of statistical learning: data mining, inference and prediction. 2nd edition, Springer-Verlag
Wickham (2016) ggplot2. Elegant Graphics for Data Analysis. 2nd Edition, Springer-Verlag
Maindonald, Braun (2010) Data Analysis and Graphics Using R: An Example-Based Approach . 3rd edition, Cambridge University Press
For Italian reading we suggest
Azzalini, Scarpa (2004) Analisi dei dati e data mining, Springer-Verlag
Methods of Provision
Teaching Methods Frontal lectures as well as practical exercises with the use of the software
R
Evaluation methods Verification of learning:
The exam is divided into a 60-minute written test (open questions with predefined space, to verify the knowledge of the theoretical part of the
topics covered in class) and in an oral presentation of a report prepared for the analysis of two different data sets using the R software.
In the examination, the two tests have the same importance and both must be overcome for the finalization of the examination. The final grade will be
given by the average of the partial scores (in thirtieths) received by the student in the two tests.
Contacts/More Information E-mail: ippoliti@unich.it
Students will be received on Tuesday between 16:00 and 18:00
Appointments can be fixed by e-mail.