| First cycle degree courses | Second cycle degree courses | Single cycle degree courses |
| School of Science | ||
| COMPUTER SCIENCE | ||
Course unit
DATA MINING
SC01111799, A.A. 2017/18
Information concerning the students who enrolled in A.Y. 2017/18
DATA MINING
SC01111799, A.A. 2017/18
Information concerning the students who enrolled in A.Y. 2017/18
Information on the course unit
| Degree course |
Second cycle degree in COMPUTER SCIENCE SC1176, Degree course structure A.Y. 2014/15, A.Y. 2017/18 |
 bring this page with you |
|---|---|---|
| Number of ECTS credits allocated | 6.0 | |
| Type of assessment | Mark | |
| Course unit English denomination | DATA MINING | |
| Website of the academic structure | http://informatica.scienze.unipd.it/2017/laurea_magistrale | |
| Department of reference | Department of Mathematics | |
| Mandatory attendance | No | |
| Language of instruction | English | |
| Branch | PADOVA |
Lecturers
| Teacher in charge | ANNAMARIA GUOLO | SECS-S/01 |
|---|
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 |
| Prerequisites: | Basic knowledge of computer science, Databases |
| Target skills and knowledge: | 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: | Written examination / Practice |
| Assessment criteria: | 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: | - 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: | The course consists of lectures and laboratory exercises on real data using the R programming language. |
| Additional notes about suggested reading: | Textbooks. Material provided by the instructor and available through the Moodle platform. |
| Textbooks (and optional supplementary readings) |
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