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Information Modeling & Analysis

Description

COURSE OBJECTIVES

We live in a world where big amounts of data are generated by multiple sources. However, data is useless unless it is properly modelled and analyzed. This course will teach students how to turn data into information, i.e., processed and organized data with meaning, by automated identification of rules, patterns and regularities.

 

COURSE DESCRIPTION

The course involves: (1) getting familiar with machine learning algorithms, their assumptions and their limitations; (2) learning how to select the training corpus and how to evaluate the outcome of training; (3) applying machine learning algorithms to a set of case studies, taken from recent research in Software Engineering. The course will cover unsupervised learning (e.g., clustering, feature maps), supervised learning (e.g., classifiers, neural networks) and concept mining (association rules, bayesian networks).

 

LEARNING METHODS

Students will be involved in practical exercises and will experiment with the presented techniques by applying them to the course projects.

 

EXAMINATION INFORMATION
Optional written mid-term exam; final oral exam; optional homework; two mandatory projects.

 

RECOMMENDED COURSES

  • Data Design & Modeling

 

REFERENCES
Lecture slides and lecture notes, available on iCorsi

People

 

Tonella P.

Course director

Weiss M.

Assistant

Additional information

Semester
Spring
Academic year
2021-2022
ECTS
6
Language
English
Education
Master of Science in Software & Data Engineering, Foundation course, 1st year
PhD programme of the Faculty of Informatics, Elective course, Lecture, 1st year (4 ECTS)
PhD programme of the Faculty of Informatics, Elective course, Lecture, 2nd year (4 ECTS)