FI:PV115 Laboratory of KD - Course Information
PV115 Laboratory of Knowledge DiscoveryFaculty of Informatics
- Extent and Intensity
- 0/0/2. 2 credit(s). Type 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
- Mon 17. 2. to Fri 15. 5. Tue 16:00–17:50 C513
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.
- 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
- 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
- Further Comments
- Study Materials
The course is taught annually.
- Teacher's information