MAT-63807 Introduction to Data Mining: The B-Course and GUHA-Method, 5 cr
Additional information
This course will not be lectured during the academiv year 2019-20. This course is an extended version of the data mining part of the former Applied Logics course lectured last time in autumn 2011. It is not possible to include both this course and Applied Logics course to one's curricula.
Suitable for postgraduate studies.
The implementation will not be executed during the academic year 2019-2020.
Person responsible
Esko Turunen
Lessons
| Implementation | Period | Person responsible | Requirements |
| MAT-63807 2019-01 | - |
Esko Turunen |
Learning Outcomes
Data mining research in perception: the ability to distinguish data mining tasks of statistical tasks. Management Bayesian method's (B-course) theoretical fundamentals. Management of GUHA method's logical-mathematical grounds. Ability to use B-course software and LISpMiner software in practical data mining tasks.
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | Introduction to data mining; does my data contain something interesting I did not know? The GUHA method: data matrices as finite models, 'almost all', 'in most cases', 'above average' and other non-standard quantifiers. Fundamentals of Bayesian reasoning in data mining | Introduction to LISpMiner, a software implementation of the GUHA method. Practical data mining tasks by LISp Miner software. Introduction and practical data mining tasks by B-course software |
Study material
| Type | Name | Author | ISBN | URL | Additional information | Examination material |
| Lecture slides | Esko Turunen | No |
Additional information about prerequisites
No individual pre-requisites, however, following the course requires a sufficient amount of mathematical thinking from the 1st, 2nd and 3rd year mathematics courses.
Correspondence of content
There is no equivalence with any other courses