PV115 Laboratory of Knowledge Discovery

Faculty of Informatics
Spring 2020
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
Mon 17. 2. to Fri 15. 5. Tue 16:00–17:50 C513
Prerequisites
SOUHLAS
Prerequisite for enrollment in the subject is 1) being familiar with basic machine learning 2) being fluent in English 2) approval of the application by the teacher Students being interested in longer than one semester collaboration will be prefered.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 77 fields of study the course is directly associated with, display
Course objectives
At the end of the course students should be able to create machine learning systems for data analysis.
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 machine learning and data science:
  • Project proposal
  • Consultation during the term
  • Presentation of results, a final report
Literature
  • FLACH, Peter A. Machine learning : the art and science of algorithms that make sense of data. New York: Cambridge University Press, 2012. xvii, 396. ISBN 1107422221. info
  • 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
  • BERKA, Petr. Dobývání znalostí z databází. Vyd. 1. Praha: Academia, 2003. 366 s. ISBN 8020010629. info
Teaching methods
Work on a project under a supervision.
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, Autumn 2020, Spring 2021.
  • Enrolment Statistics (Spring 2020, recent)
  • Permalink: https://is.muni.cz/course/fi/spring2020/PV115