PV115 Laboratory of Knowledge Discovery

Faculty of Informatics
Autumn 2023
Extent and Intensity
0/0/2. 2 credit(s). Type of Completion: z (credit).
Teacher(s)
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
Timetable
Tue 15:00–16:50 A321
Prerequisites (in Czech)
SOUHLAS
Předpokladem pro zápis do předmětu je 1) schopnost samostatné práce v oblasti ML 2) schválení přihlášky vedoucím laboratoře (kapacita labu i vedoucího spíše omezená) 3) schopnost práce v týmu; Zájemci o dlouhodobější zapojení či spoluprácující na projektech laboratoře mají přednost.
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 10 student(s).
Current registration and enrolment status: enrolled: 4/10, only registered: 0/10, only registered with preference (fields directly associated with the programme): 0/10
fields of study / plans the course is directly associated with
Course objectives
At the end of the course students should be able to create systems for knowledge discovery in data.
Learning outcomes
A student will be able
- to understand research papers from machine learning and data mining;
- of critical reading of such papers;
- to build and validate a machine learning or data mining method.
Syllabus
  • Students participate on research projects in various areas of knowledge discovery and data mining:
  • Project proposal
  • Consultation during the term
  • Presentation of results, a final report It is appropriate for those who look for help in solving more complex tasks of machine learning and data mining.
Literature
    recommended literature
  • Peter Flach: Machine learning : the art and science of algorithms that make sense of data. Cambridge ; New York : Cambridge University Press, c2012
  • HAN, Jiawei and Micheline KAMBER. Data mining : concepts and techniques. 2nd ed. San Francisco, CA: Morgan Kaufmann, 2006, xxviii, 77. ISBN 1558609016. URL info
Teaching methods
Work on a project under a supervision of the head of the laboratory.
Assessment methods
A project defense, a credit
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://www.fi.muni.cz/kd/
The course is also listed under the following terms Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, 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, Spring 2020, Autumn 2020, Spring 2021, Autumn 2021, Spring 2022, Autumn 2022, Spring 2023, Spring 2024, Autumn 2024, Spring 2025.
  • Enrolment Statistics (Autumn 2023, recent)
  • Permalink: https://is.muni.cz/course/fi/autumn2023/PV115