PV056 Machine Learning and Data Mining

Faculty of Informatics
Spring 2025
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).
In-person direct teaching
Teacher(s)
doc. RNDr. Jan Sedmidubský, Ph.D. (lecturer)
doc. RNDr. Lubomír Popelínský, Ph.D. (lecturer)
RNDr. Ondřej Sotolář (assistant)
Guaranteed by
doc. RNDr. Lubomír Popelínský, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Contact Person: doc. RNDr. Jan Sedmidubský, Ph.D.
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics
Prerequisites
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 37 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;
- build and validate an advanced machine learning/data mining method;
- to write a technical report.
Syllabus
  • 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.
  • Advanced machine learning methods. Ensemble learning. AutoML, preference learning. Multi-relational learning.
  • Mining frequent patterns and association rules. Apriori algorithm. Frequent patterns in multi-relational data.
  • Anomaly analysis.
  • Preprocessing. Feature selection and construction. Sampling
  • Active learning. Semi-supervised learning
  • Temporal data mining.
Literature
    recommended literature
  • HAN, Jiawei and Micheline KAMBER. Data mining : concepts and techniques. 2nd ed. San Francisco, CA: Morgan Kaufmann, 2006, xxviii, 77. ISBN 1558609016. URL 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
  • BERKA, Petr. Dobývání znalostí z databází. Vyd. 1. Praha: Academia, 2003, 366 s. ISBN 8020010629. info
  • Relational data mining. Edited by Sašo Džeroski - Nada Lavrač. Berlin: Springer, 2001, xix, 398. ISBN 3540422897. 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
English
Further Comments
The course is taught annually.
The course is taught: every week.
Listed among pre-requisites of other courses
Teacher's information
http://www.fi.muni.cz/usr/popelinsky/lectures/kdd/
The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024.
  • Enrolment Statistics (Spring 2025, recent)
  • Permalink: https://is.muni.cz/course/fi/spring2025/PV056