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Course unit
DATA MINING
SC01111799, A.A. 2017/18
Information concerning the students who enrolled in A.Y. 2017/18
ECTS: details
Type |
Scientific-Disciplinary Sector |
Credits allocated |
Educational activities in elective or integrative disciplines |
SECS-S/01 |
Statistics |
6.0 |
Mode of delivery (when and how)
Period |
Second semester |
Year |
1st Year |
Teaching method |
frontal |
Organisation of didactics
Type of hours |
Credits |
Hours of teaching |
Hours of Individual study |
Shifts |
Laboratory |
2.0 |
16 |
34.0 |
No turn |
Lecture |
4.0 |
34 |
66.0 |
No turn |
Start of activities |
26/02/2018 |
End of activities |
01/06/2018 |
Examination board
Examination board not defined
Prerequisites:
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Basic knowledge of computer science, Databases |
Target skills and knowledge:
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The course is intended to provide an overview of concepts and methods for data analysis, as well as of instruments useful for a critical evaluation of the results. |
Examination methods:
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Written examination / Practice |
Assessment criteria:
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The exam has the aim of evaluating the knowledge acquired by the students and its application to the analysis of a dataset. |
Course unit contents:
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- Introduction to the course: Data analysis as a tool for decision support. Motivations and context for data mining.
- Linear and generalised linear predictive models
- Classification methods: logistic regression, linear discriminant analysis and extensions
- Cross validation
- Model selection and regularisation
- Nonlinear models: semi-parametric and non-parametric regression
- Tree-based methods |
Planned learning activities and teaching methods:
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The course consists of lectures and laboratory exercises on real data using the R programming language. |
Additional notes about suggested reading:
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Textbooks. Material provided by the instructor and available through the Moodle platform. |
Textbooks (and optional supplementary readings) |
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Azzalini A., Scarpa B., Analisi dei dati e data mining. --: Springer, 2004.
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Gareth, J., Witten, D., Hastie, T., Tibshirani, R., An Introduction to Statistical Learning with Applications in R. --: Springer, 2013.
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