FI:IA080 Seminar on Knowledge Discovery - Course Information
IA080 Seminar on Knowledge DiscoveryFaculty of Informatics
- Extent and Intensity
- 0/2. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: k (colloquium). Other types of completion: z (credit).
- doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
- Guaranteed by
- doc. RNDr. Lubomír Popelínský, Ph.D.
Department of Machine Learning and Data Processing - Faculty of Informatics
Contact Person: doc. RNDr. Lubomír Popelínský, Ph.D.
Supplier department: Department of Machine Learning and Data Processing - Faculty of Informatics
- Prerequisite for enrollment in the subject is 1) being familiar with advanced machine learning 2) approval of the application by the teacher
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 15 student(s).
Current registration and enrolment status: enrolled: 0/15, only registered: 2/15, only registered with preference (fields directly associated with the programme): 0/15
- Fields of study the course is directly associated with
- there are 49 fields of study the course is directly associated with, display
- Course objectives
- At the end of the course students should be able to build and evaluate advanced machine learning systems and to understand scientific works in the area of machine learning and data science and use it in their work. They will be able to evaluate contributions of such research studies.
- Learning outcomes
- A student will be able
- to understand research papers from machine learning and data mining;
- of critical reading of such papers;
- to prepare and present a lecture on advanced methods of data science.
- The seminar is focused on machine learning and theory and practice of knowledge discovery in various data sources. Program of the seminar contains also contributions of teachers and PhD. students of the Knowldge Discovery Laboratory, as well as other laboratories, on advanced topics of knowledge discovery.
- PROVOST, Foster and Tom FAWCETT. Data science for business : what you need to know about data mining and data-analytic thinking. 1st ed. Beijing: O'Reilly, 2013. xxi, 386. ISBN 9781449361327. info
- HAN, Jiawei and Micheline KAMBER. Data mining : concepts and techniques. 2nd ed. San Francisco, CA: Morgan Kaufmann, 2006. xxviii, 77. ISBN 1558609016. info
- Teaching methods
- Presentations by staff members and PhD. students. Study of research papers and presentation of advanced methods for machine learning and data mining.
- Assessment methods
- Presentation of an advanced topic from machine learning, data mining and knowledge discovery, a final report.
- Language of instruction
- Further Comments
- The course is taught each semester.
The course is taught: every week.
- Teacher's information
- Enrolment Statistics (recent)
- Permalink: https://is.muni.cz/course/fi/spring2020/IA080