IA080 Seminar on Knowledge Discovery

Faculty of Informatics
Spring 2020
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
The course is also listed under the following terms Autumn 2002, Spring 2003, Autumn 2003, Spring 2004, Autumn 2004, Spring 2005, Autumn 2005, Spring 2006, Autumn 2006, Spring 2007, Autumn 2007, Spring 2008, Autumn 2008, Spring 2009, Autumn 2009, Spring 2010, Autumn 2010, Spring 2011, Autumn 2011, Spring 2012, Autumn 2012, Spring 2013, Autumn 2013, Spring 2014, Autumn 2014, Spring 2015, Autumn 2015, Spring 2016, Autumn 2016, Spring 2017, Autumn 2017, Spring 2018, Autumn 2018, Spring 2019.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/fi/spring2020/IA080