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66701 - Computational Mathematics

Academic Year 2022/2023

                
                        Docente:
                        Valeria Simoncini
                    
                        Credits:
                        6
                    
                        SSD:
                        MAT/08
                    
                        Language:
                        Italian
                    
                        Teaching Mode:
                        Traditional lectures
                        
                            Campus:
                            Bologna
                        
                            Corso:
                            First cycle degree programme (L) in
                            Mathematics (cod. 8010)

                            Teaching resources on Virtuale

Learning outcomes

At the end of the course, the student has skills, both theoretical and computational, to solve some basic numerical problems in the applications in Data Mining and the analysis of Big Data.

Course contents

The course includes the study of: Matrix methods for Data Mining. The information contained in large amounts of data, used for example by search engines (eg Google), or used in the study of climatic data, pattern recognition, etc., is often manageable thanks to the use of matrix techniques advanced high-level, for the numerical resolution of large-scale linear systems, the numerical resolution of eigenvalue problems and large singular values, the calculation of matrix functions, and the management of graphs. The course involves studying these techniques, starting from the analytical aspects of Matrix Theory, and arriving at their practical use in Data Mining.

Readings/Bibliography

Lars Elden, Matrix Methods in Data Mining and Pattern Recognition, SIAM, Aprile 2007.
M.W. Berry and M. Browne, Understanding Search Engines: Mathematical Modeling and Text Retrieval , SIAM Book Series: Software, Environments, and Tools, Second Edition (Maggio 2005).
More textbooks, recent scientific articles anda case studies from real world applications.

Teaching methods

Blackboard, slides and computer lab sessions.

Assessment methods

Oral presentation of a takehome project (with slides).

Possible lab test on the computer lab sessions.

Teaching tools

Slides made available as pdf file on the course webpage. Use of Matlab computational environment, and various toolboxes.

See http://www.dm.unibo.it/~simoncin/DataMining.html

Links to further information

http://www.dm.unibo.it/~simoncin/DataMining.html

Office hours

See the website of Valeria Simoncini