12569 - Computational Mathematics

Learning outcomes

At the end of the course, students have theoretical and computational skills to solve some fundamental numerical problems arising in data science, by using matrix and tensor techniques. Students can implement and analyze the studied methods from a theoretical and applied point of view.

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 and case studies from real world applications.

 

more material at:

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

Teaching methods

Blackboard, slides and computer lab sessions.

Assessment methods

Oral presentation of a takehome project (with slides), and oral exam on course material

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

Office hours

See the website of Valeria Simoncini

See the website of Martina Iannacito