UIDI005 Machine Learning and Knowledge Acquisition Methods

Faculty of Philosophy and Science in Opava
Summer 2023
Extent and Intensity
0/0/0. 0 credit(s). Type of Completion: dzk.
Guaranteed by
prof. Ing. Petr Berka, CSc.
Institute of Computer Science - Faculty of Philosophy and Science in Opava
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 objective of the course is to get familiar with basic terms and methods of machine learning and knowledge acquisition which can be applied for the development of intelligent systems. The main emphasis is on symbolic methods of machine learning from data.
  • 1. The role of knowledge in intelligent systems.
    2. Resources for knowledge acquisition: experts, texts, examples of decisions, observation data.
    3. Methods of knowledge acquisition from experts. Knowledge modelling, the KADS methodology.
    4. Definition, goals and history of machine learning. Representation of knowledge and data, types of goal concepts.
    5. Computational complexity of algorithms of machine learning, the learnability theory.
    6. Quality of the acquired knowledge, the role of data and knowledge.
    7. Induction of the decision trees, induction of rules from data.
    8. The GUHA method, combinatorial data analysis.
    9. The set covering method. Concept clustering. Unsupervised learning.
    10. Learning of concepts in the first order language. Inductive logic programming.
    11. Connectionist learning. Genetic algorithms.
    12. Case-Based Reasoning. Knowledge mining from databases.
Teaching methods
Interactive lecture
Lecture with a video analysis
Assessment methods
Language of instruction
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
Teacher's information
A literature review of selected topics of machine learning and knowledge acquisition methods due to the teacher's selection related to the PhD thesis of the student. An oral exam - minimum success rate 50%.
The course is also listed under the following terms Winter 2006, Summer 2007, Winter 2007, Summer 2008, Winter 2008, Summer 2009, Winter 2009, Summer 2010, Winter 2010, Summer 2011, Winter 2011, Summer 2012, Winter 2012, Summer 2013, Winter 2013, Summer 2014, Winter 2014, Summer 2015, Winter 2015, Summer 2016, Winter 2016, Summer 2017, Winter 2017, Summer 2018, Winter 2018, Summer 2019, Winter 2019, Summer 2020, Winter 2020, Summer 2021, Winter 2021, Summer 2022.
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  • Permalink: https://is.slu.cz/course/fpf/summer2023/UIDI005