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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: - getting familiar with machine learning algorithms, their assumptions and their limitations; - learning how to select the training corpus and how to evaluate the outcome of training; - 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 midterm exam; oral final exam.

 

RECOMMENDED COURSES

  • Data Design & Modeling

 

REFERENCES

  • Lecture slides and lecture notes, available on iCorsi

People

 

Tonella P.

Course director

Additional information

Semester
Spring
Academic year
2020-2021
ECTS
6
Language
English
Education
Master of Science in Software & Data Engineering, Core course, Lecture, 1st year