Bi0034 Knowledge Discovery by Machine Learning

Faculty of Science
Autumn 2007 - for the purpose of the accreditation
Extent and Intensity
2/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
doc. Ing. Jan Žižka, CSc. (lecturer)
RNDr. Danka Haruštiaková, Ph.D. (assistant)
Guaranteed by
doc. Ing. Jan Žižka, CSc.
RECETOX – Faculty of Science
Contact Person: doc. Ing. Jan Žižka, CSc.
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
Course objectives
The subject concerns with inductive machine-learning methods using data samples. It explains algorithms, their principles, possibilities, and applications to automated non-analytic knowledge discovery in real-world data. The application capabilities are in looking for similar instances, further in classification, regression, and prediction.
Syllabus
  • The relationships among data, information, and knowledge. Inductive learning. Automated knowledge discovery from information by pattern generalization. Training and testing, pattern selection and their representation. Problems connected with real data and incomplete descriptions of patterns, compensation of missing values and samples. Advanced fundamental algorithms of machine learning. Computational complexity, its approximation. Unsupervised learning (clustering) and supervised learning (classification, regression), pattern recognition. Interdisciplinary relations, application dependencies. Data preprocessing, algorithm selection, design and evaluation of experiments. Practical experiments with real data and the software system of machine-learning tools WEKA.
Literature
  • Duda, R. O., Hart, P. E., Stork, D. G. (2001) Pattern Classification. Second edition. John Wiley & Sons. ISBN 0-471-05669-3
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is taught: every week.
The course is also listed under the following terms Autumn 2010 - only for the accreditation, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021.