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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, matrices, 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 Statistical Learning, data visualization, regression and classification,
Non-supervised learning (principal component analysis, Clustering, procruste), Analysis if complex data (spatial data and social data mining)
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:
1. Introduction to data mining e statistical learning.
2. Data Matrix and object oriented data analysis (OODA)
3. Data visualization technique
4. Introduction to probability
5. The multivariate Normal Distribution
6. Prediction model for independent data with R (LDA, K-NN, SVM)
7. Analysis of complex data (Object Oriented Data Analysis) con R
7.1 OODA and Procrustes analysis
7.2 OODA and spatial data analysis
7.3 OODA and Social Data Mining
Recommended Bibliography - Slides and handouts for students not attending the course will be available from the professor
- Maindonald, Braun (2010) Data Analysis and Graphics Using R: An Example-Based Approach. 3rd edition, Cambridge University Press
- James, Witten, Hastie, Tibshirani (2013) An Introduction to Statistical Learning (with Applications in R), Springer-Verlag
- Kevin Murphy (2012) Machine learning : a probabilistic perspective, The MIT Press, Cambridge, Massachusetts, London, England
- Luìs Torgo (2011) Data Mining with R. Learning with case studies. CRC Press
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 passed 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 Mon and Wed between 15:00 and 16:00
Appointments can be fixed by e-mail.