FI:PV056 Machine Learning - Course Information
PV056 Machine Learning and Data MiningFaculty of Informatics
- Extent and Intensity
- 2/0/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
Bc. Dušan Hetlerović (assistant)
- 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 Thu 7. 5. Wed 8:00–9:50 A318; and Wed 13. 5. 8:00–9:50 B517
- A student needs to be familiar with basics of machine learning (e.g. IB031 Introduction to machine learning).
- 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 74 fields of study the course is directly associated with, display
- Course objectives
- At the end of the course students should be able to use machine learning and data mining methods. They will be able to built tools for mining in data that employ machine learning methods.
- Learning outcomes
- A student will be able
- to pre-process data for data mining;
- to know advanced method of machine learning and data mining and to use them;
- to write a technical report;
- build and validate an advanced machine learning/data mining method.
- Introduction to the theory of knowledge discovery in databases. Survey of the most important methods, algorithms and systems. A project is as a part of the course.
- Knowledge discovery in databases. Data mining.
- Basic algorithms of machine learning.
- Mining frequent patterns and association rules.
- Data visualization, visual analytics
- Text mining, mining in spatio-temporal dat, web mining.
- Data mining and life sciences.
- recommended literature
- HAN, Jiawei and Micheline KAMBER. Data mining : concepts and techniques. 2nd ed. San Francisco, CA: Morgan Kaufmann, 2006. xxviii, 77. ISBN 1558609016. info
- Relational data mining. Edited by Sašo Džeroski - Nada Lavrač. Berlin: Springer, 2001. xix, 398. ISBN 3540422897. info
- BERKA, Petr. Dobývání znalostí z databází. Vyd. 1. Praha: Academia, 2003. 366 s. ISBN 8020010629. info
- not specified
- 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
- Teaching methods
- Lectures, a project.
- Assessment methods
- Written and oral exam. A defense of a project is as a part of the exam.
- Language of instruction
- Further Comments
- Study Materials
The course is taught annually.
- Teacher's information
- Enrolment Statistics (recent)
- Permalink: https://is.muni.cz/course/fi/spring2020/PV056